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    "# Learning to Control Indoor Air Temperature in Buildings With Safe Reinforcement Learning and Differentiable Predictive Control\n",
    "\n",
    "This notebook demonstrates and compares two advanced methods for controlling indoor air temperature in a single-zone building:\n",
    "- **Primal-Dual Deep Deterministic Policy Gradient (PD-DDPG)**: A Safe Reinforcement Learning (RL) method tailored for constrained optimization in dynamic environments.\n",
    "- **Differentiable Predictive Control (DPC)**: A model-based offline policy optimization approach leveraging differentiable system dynamics.\n",
    "---\n",
    "## Motivation\n",
    "Buildings today contribute to roughly 40% of the global energy use (approx. 64 PWh), of which a large portion is used for [heating, cooling, ventilation, and air-conditioning (HVAC)](https://en.wikipedia.org/wiki/Heating,_ventilation,_and_air_conditioning) [4]. It has been demonstrated that advanced building control, like [model predictive control (MPC)](https://en.wikipedia.org/wiki/Model_predictive_control)\n",
    "or [deep reinforcement learning (DRL)](https://en.wikipedia.org/wiki/Deep_reinforcement_learning), can notably reduce the energy use and mitigate greenhouse gas emissions. However, despite intensive research efforts, the practical applications are still in the early stages. One of these challenges is the complexity of the optimal control technology to be deployed in the current predominantly rule-based [building automation systems](https://en.wikipedia.org/wiki/Building_automation).\n",
    "\n",
    "  \n",
    "Below is a simplified representation of a building control scheme governed by advanced optimization algorithms:\n",
    "<img src=\"./figs/building_control.PNG\" width=\"500\">\n",
    "\n",
    "---\n",
    "## Objectives\n",
    "1. Train a PD-DDPG agent for building temperature regulation task.\n",
    "2. Implement DPC for the same problem and compare:\n",
    "   - Energy consumption.\n",
    "   - Referenece temperature constraint violations.\n",
    "   - Training time.\n",
    "3. Provide insights into the strengths and weaknesses of both methods.\n",
    "---\n",
    "## Differentiable Predictive Control (DPC)\n",
    "\n",
    "[Differentiable Predictive Control](https://www.sciencedirect.com/science/article/pii/S0959152422000981) (DPC) is a model-based offline optimization algorithm that leverages differentiable system models to train constrained control policies. \n",
    "\n",
    "**Key Features of DPC**:\n",
    "- **Differentiable system model**:  \n",
    "The DPC is a model-based policy optimization algorithm, that exploits the differentiability of a wide class of model representations for dynamical systems, including differential equations, state-space models, or various neural network architectures. In this example, we compactly represent the system model by ODE equations  $\\text{ODESolve}(f(x^i_k, u^i_k))$  describing the governing dynamics of the controlled system. \n",
    "- **Constrained Neural Policy**: Learns a control policy $u_k = \\pi(x_k, R, d)$ that minimizes energy use while ensuring temperature constraints are satisfied. The policy uses:\n",
    "  - $x_k$: System states (e.g., indoor temperature).\n",
    "  - $R$: Reference trajectory (temperature bounds over the prediction horizon).\n",
    "  - $d_k$: Disturbances (e.g. Solar radiation)\n",
    "- **Optimization Problem**: Solves a parametric optimization problem to minimize the tracking error and energy use:\n",
    "  $$\n",
    "\\begin{align}\n",
    "&\\underset{\\theta}{\\text{minimize}}     && \\sum_{i=1}^m  \\Big( \\sum_{k=1}^{N-1} Q_x||x^i_k - r^i_k||_2^2  + Q_N||u^i_k||_2^2 \\Big) \\\\\n",
    "&\\text{subject to}    && x^i_{k+1} =  \\text{ODESolve}(f(x^i_k, u^i_k)) \\\\\n",
    "&                     && u^i_k = \\pi_{\\theta}(x^i_k, R^i, d^i_k) \\\\\n",
    "&                     && 0 \\le x^i_k \\le 1 \\\\\n",
    "&                     && 0 \\le u^i_k \\le 1 \\\\\n",
    "&                     && x^i_0 \\sim \\mathcal{P}_{x_0} \\\\\n",
    "&                     && R^i \\sim  \\mathcal{P}_R\n",
    "\\end{align}\n",
    "$$  \n",
    "\n",
    "DPC combines the advantages of model-based optimization with neural network policy flexibility, making it a powerful tool for control tasks like building temperature regulation.\n",
    "- **Schematics of the Differentiable Predictive Control method [[1]](https://www.sciencedirect.com/science/article/pii/S0959152422000981)**:  \n",
    "\n",
    "<p align=\"center\">\n",
    "  <img src=\"https://ars.els-cdn.com/content/image/1-s2.0-S0959152422000981-gr4_lrg.jpg\" width=\"500\">  \n",
    "</p>\n",
    "\n",
    "---\n",
    "\n",
    "\n",
    "## Primal-Dual Deep Deterministic Policy Gradient (PD-DDPG)\n",
    "\n",
    "[PD-DDPG](https://arxiv.org/abs/1802.06480) is a reinforcement learning algorithm designed to handle **Constrained Markov Decision Processes (CMDPs)**. It extends the [Deep Deterministic Policy Gradient (DDPG)](https://arxiv.org/abs/1509.02971) method by incorporating a primal-dual optimization framework to address constraints.\n",
    "PD-DDPG excels in scenarios with hard constraints, such as maintaining temperature within predefined comfort bounds while minimizing energy use. We will explain the mathematical details further during the coding sections of this algorithm.\n",
    "\n",
    "\n",
    "\n",
    "---\n",
    "## Expected Outcomes\n",
    "- A fair comparison of PD-DDPG and DPC for building control tasks.\n",
    "- Recommendations for future improvements in these methods.\n",
    "- Insights into the trade-offs between RL and model-based control approaches.\n",
    "---\n",
    "## References\n",
    "### DPC References\n",
    "[1] [Ján Drgoňa, Karol Kiš, Aaron Tuor, Draguna Vrabie, Martin Klaučo,\n",
    "Differentiable predictive control: Deep learning alternative to explicit model predictive control for unknown nonlinear systems,\n",
    "Journal of Process Control, Volume 116, 2022](https://www.sciencedirect.com/science/article/pii/S0959152422000981)  \n",
    "[2] [Jan Drgona, Aaron Tuor, Draguna Vrabie, Learning Constrained Adaptive Differentiable Predictive Control Policies With Guarantees, 2020, arXiv:2004.11184](https://arxiv.org/abs/2004.11184)  \n",
    "[3] [Ján Drgoňa, Aaron Tuor, Elliott Skomski, Soumya Vasisht, Draguna Vrabie,\n",
    "Deep Learning Explicit Differentiable Predictive Control Laws for Buildings,\n",
    "IFAC-PapersOnLine,\n",
    "Volume 54, Issue 6,\n",
    "2021](https://www.sciencedirect.com/science/article/pii/S2405896321012933)\n",
    "\n",
    "### Related Building Control References\n",
    "[4] [Ján Drgoňa, Javier Arroyo, Iago Cupeiro Figueroa, David Blum, Krzysztof Arendt, Donghun Kim, Enric Perarnau Ollé, Juraj Oravec, Michael Wetter, Draguna L. Vrabie, Lieve Helsen,\n",
    "All you need to know about model predictive control for buildings,\n",
    "Annual Reviews in Control,\n",
    "Volume 50,\n",
    "2020](https://www.sciencedirect.com/science/article/pii/S1367578820300584)  \n",
    "[5] [Ján Drgoňa, Damien Picard, Lieve Helsen,\n",
    "Cloud-based implementation of white-box model predictive control for a GEOTABS office building: A field test demonstration,\n",
    "Journal of Process Control,\n",
    "Volume 88,\n",
    "2020](https://www.sciencedirect.com/science/article/pii/S0959152419306857)  \n",
    "[6] [Zoltan Nagy, Gregor Henze, Sourav Dey, Javier Arroyo, Lieve Helsen, Xiangyu Zhang, Bingqing Chen, Kadir Amasyali, Kuldeep Kurte, Ahmed Zamzam, Helia Zandi, Ján Drgoňa, Matias Quintana, Steven McCullogh, June Young Park, Han Li, Tianzhen Hong, Silvio Brandi, Giuseppe Pinto, Alfonso Capozzoli, Draguna Vrabie, Mario Bergés, Kingsley Nweye, Thibault Marzullo, Andrey Bernstein,\n",
    "Ten questions concerning reinforcement learning for building energy management,\n",
    "Building and Environment,\n",
    "Volume 241,\n",
    "2023](https://www.sciencedirect.com/science/article/abs/pii/S0360132323004626)  \n",
    "[7] [Bingqing Chen, Priya L. Donti, Kyri Baker, J. Zico Kolter, and Mario Bergés. 2021. Enforcing Policy Feasibility Constraints through Differentiable Projection for Energy Optimization. In Proceedings of the Twelfth ACM International Conference on Future Energy Systems (e-Energy '21)](https://dl.acm.org/doi/abs/10.1145/3447555.3464874)  \n",
    "[8] [Jan Široký, Frauke Oldewurtel, Jiří Cigler, Samuel Prívara,\n",
    "Experimental analysis of model predictive control for an energy efficient building heating system,\n",
    "Applied Energy,\n",
    "Volume 88, Issue 9,\n",
    "2011](https://www.sciencedirect.com/science/article/pii/S0306261911001668)\n",
    "\n",
    "\n",
    "### Related Deep Reinforcement Learning References\n",
    "[9] [Liang Q, Que F, Modiano E. Accelerated primal-dual policy optimization for safe reinforcement learning. arXiv preprint arXiv:1802.06480. 2018 Feb 19.](https://arxiv.org/abs/1802.06480)  \n",
    "[10] [Lillicrap TP. Continuous control with deep reinforcement learning. arXiv preprint arXiv:1509.02971. 2015.](https://arxiv.org/abs/1509.02971)\n"
   ]
  },
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   "metadata": {},
   "source": [
    "## For Colab only:"
   ]
  },
  {
   "cell_type": "code",
   "id": "ae239d7f551fa153",
   "metadata": {},
   "source": "!pip install neuromancer gym",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "*Note: When running on Colab, one might encounter a pip dependency error with Lida 0.0.10. This can be ignored*",
   "id": "145fc5fa22ec829c"
  },
  {
   "cell_type": "code",
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "ExecuteTime": {
     "end_time": "2025-01-31T01:51:19.598801Z",
     "start_time": "2025-01-31T01:51:19.596026Z"
    }
   },
   "source": [
    "import time\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "from datetime import datetime\n",
    "from collections import deque\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n"
   ],
   "outputs": [],
   "execution_count": 46
  },
  {
   "cell_type": "markdown",
   "id": "3f6f9017d538fb69",
   "metadata": {},
   "source": [
    "## Generating Training Data for RL and DPC Controllers\n",
    "\n",
    "The `generate_data()` function simulates the dynamics of a single-zone building, enabling the creation of structured datasets for reinforcement learning (RL) agents and Dynamic Programming Controllers (DPC). The function relies on a **linear state-space model** to approximate the behavior of the building:\n",
    "\n",
    "$$\n",
    "x_{t+1} = A \\, x_t + B \\, u_t + E \\, d_t, \\quad y_t = C \\, x_t,\n",
    "$$\n",
    "\n",
    "where:\n",
    "- **`x_t`**: State vector (e.g., indoor temperature).\n",
    "- **`u_t`**: Control action (bounded by `umin` and `umax`).\n",
    "- **`d_t`**: External disturbances (e.g., outdoor temperature, solar radiation).\n",
    "- **`A`, `B`, `C`, `E`**: System matrices describing the dynamics.\n",
    "\n",
    "---\n",
    "\n",
    "### Steps in `generate_data()`\n",
    "\n",
    "1. **System Initialization**: The `LinearSimpleSingleZone` model is loaded from NeuroMANCER to extract matrices (`A`, `B`, `C`, `E`) and control bounds (`umin`, `umax`).\n",
    "2. **Helper Function (`create_dataset`)**:\n",
    "   - Generates references (`ymin`, `ymax`) for target temperature ranges.\n",
    "   - Samples disturbance trajectories (`d`) and initial conditions (`x0`).\n",
    "   - Returns a structured `DictDataset` for training or validation.\n",
    "3. **Dataset Creation**:\n",
    "   - `train_data`: Dataset for training, using `ref_range` as the reference offset.\n",
    "   - `dev_data`: Dataset for validation, using the same reference offset.\n",
    "4. **Data Loaders**:\n",
    "   - Converts `train_data` and `dev_data` into PyTorch data loaders for batch processing.\n",
    "5. **Normalization Statistics**:\n",
    "   - Computes mean and standard deviation for features (`x`, `ymin`, `ymax`, `d`) to normalize the data during training.\n",
    "\n",
    "---\n",
    "\n",
    "### Outputs\n",
    "\n",
    "1. **`problem_specs`**: Contains system properties like state dimensions, control bounds, and system matrices.\n",
    "2. **`data_stats`**: Mean and standard deviation for normalization.\n",
    "3. **`data`**: Includes structured `train_data` and `dev_data` datasets.\n",
    "4. **`data_loader`**: PyTorch data loaders for efficient batch access.\n",
    "\n",
    "---\n",
    "\n",
    "### Example\n",
    "\n",
    "```python\n",
    "problem_specs, data_stats, data, data_loader = generate_data(\n",
    "    nsteps=50, n_samples=100, x_min=18.0, x_max=24.0, ref_range=2.0, batch_size=32\n",
    ")\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "id": "183f98277d95a984",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-31T01:28:16.022330Z",
     "start_time": "2025-01-31T01:28:15.774671Z"
    }
   },
   "source": [
    "import neuromancer.psl as psl\n",
    "from neuromancer.dataset import DictDataset\n",
    "import torch\n",
    "\n",
    "def generate_data(nsteps, n_samples, x_min, x_max, ref_range, batch_size):\n",
    "    sys = psl.systems['LinearSimpleSingleZone']()\n",
    "    \n",
    "    # Extract system dynamics and dimensions\n",
    "    A, B, C, E = map(torch.tensor, (sys.params[2]['A'], sys.params[2]['Beta'], sys.params[2]['C'], sys.params[2]['E']))\n",
    "    umin, umax = torch.tensor(sys.umin), torch.tensor(sys.umax)\n",
    "    nx, nu, nd, ny, nref = sys.nx, sys.nu, E.shape[1], sys.ny, sys.ny\n",
    "    \n",
    "    problem_specs = {\n",
    "        \"nx\": nx, \"nu\": nu, \"nd\": nd, \"ny\": ny, \"nref\": nref,\n",
    "        \"umin\": umin, \"umax\": umax, \"A\": A, \"B\": B, \"C\": C, \"E\": E\n",
    "    }\n",
    "\n",
    "    # Helper function to generate dataset\n",
    "    def create_dataset(ref_offset, num_samples, name):\n",
    "        ymin = torch.cat([\n",
    "            x_min + (x_max - x_min) * torch.rand(1, 1) * torch.ones(nsteps, nref)\n",
    "            for _ in range(num_samples)\n",
    "        ]).reshape(num_samples, nsteps, nref)\n",
    "        ymax = ymin + ref_offset\n",
    "        dist = torch.stack([torch.tensor(sys.get_D(nsteps)) for _ in range(num_samples)], dim=0)\n",
    "        x0 = torch.stack([torch.tensor(sys.get_x0().reshape(1, nx)) for _ in range(num_samples)], dim=0)\n",
    "        return DictDataset({'x': x0, 'ymin': ymin, 'ymax': ymax, 'd': dist}, name=name)\n",
    "    \n",
    "    # Generate training and development datasets\n",
    "    train_data = create_dataset(ref_range, n_samples, \"train\")\n",
    "    dev_data = create_dataset(ref_range, n_samples // 10, \"dev\")\n",
    "    \n",
    "    # Data loaders\n",
    "    train_loader = torch.utils.data.DataLoader(train_data, batch_size=batch_size,\n",
    "                                               collate_fn=train_data.collate_fn, shuffle=False)\n",
    "    dev_loader = torch.utils.data.DataLoader(dev_data, batch_size=batch_size,\n",
    "                                             collate_fn=dev_data.collate_fn, shuffle=False)\n",
    "    \n",
    "    # Normalization statistics\n",
    "    all_data = train_data.datadict  # Access the internal data dictionary directly\n",
    "    means = {k: v.mean(dim=[0, 1]) for k, v in all_data.items()}\n",
    "    stds = {k: v.std(dim=[0, 1]) for k, v in all_data.items()}\n",
    "    \n",
    "    data_stats = {\"means\": torch.cat(list(means.values())), \"stds\": torch.cat(list(stds.values()))}\n",
    "    data = {\"train_data\": train_data, \"dev_data\": dev_data}\n",
    "    data_loader = {\"train_loader\": train_loader, \"dev_loader\": dev_loader}\n",
    "    \n",
    "    return problem_specs, data_stats, data, data_loader\n"
   ],
   "outputs": [],
   "execution_count": 5
  },
  {
   "cell_type": "markdown",
   "id": "cf45349df2694a53",
   "metadata": {},
   "source": [
    "## SimpleBuildingEnv: A Custom Gym Environment for Indoor Temperature Control\n",
    "\n",
    "`SimpleBuildingEnv` is a custom RL environment built on [OpenAI Gym](https://www.gymlibrary.dev/index.html) to regulate indoor temperature efficiently. RL works by training an agent to interact with an environment, learn through trial and error, and optimize its actions to maximize rewards over time. Each RL problem is unique, so we define custom environments like this one to capture specific dynamics and constraints.\n",
    "\n",
    "---\n",
    "\n",
    "\n",
    "### RL Terminology:\n",
    "- **Agent**: The decision-maker that takes actions in the environment.\n",
    "- **Environment**: The system the agent interacts with, defined by states, actions, and rewards.\n",
    "- **State**: The current representation of the system (e.g., indoor temperature, disturbances in our case).\n",
    "- **Action**: A decision made by the agent (e.g., adjusting HVAC controls).\n",
    "- **Reward**: Feedback given to the agent for its actions on how well its performing (e.g., penalizing energy usage).\n",
    "- **Cost (in Safe RL)**: Feedback that measures how much the agent's actions violate safety constraints. Unlike rewards, which the agent seeks to maximize, costs are minimized to ensure the agent's behavior adheres to safety or operational limits (e.g., maintaining temperature within comfortable bounds).\n",
    "- **Policy**: The agent’s strategy for selecting actions based on the current state.\n",
    "\n",
    "This environment uses a linear state-space model for building dynamics and leverages the `generate_data()` function to create realistic training datasets for RL agents and controllers.\n",
    "\n",
    "\n",
    "\n",
    "### How RL Works\n",
    "\n",
    "The agent interacts with the environment in a loop:\n",
    "\n",
    "1. **Observation**: The agent observes the current state of the environment (e.g., indoor temperature, external disturbances, and reference).\n",
    "2. **Action Selection**: Based on the current state, the agent selects an action (e.g., adjusting HVAC mass flow rate).\n",
    "3. **Transition**: The environment transitions to a new state based on the action and its internal dynamics.\n",
    "4. **Getting Reward and Cost**: The agent receives feedback on its action's effectiveness (e.g., minimizing energy usage and constraint violation).\n",
    "5. **Policy Update**: The agent evaluates and improves its decision-making policy using collected rewards.\n",
    "\n",
    "This process is illustrated in the diagram below:\n",
    "\n",
    "\n",
    "<p align=\"center\">\n",
    "  <img src=\"https://miro.medium.com/v2/resize:fit:1400/1*G-_27UjSJpZ7nY19w7OOCg.png\" width=\"500\">\n",
    "</p>\n",
    "\n",
    "\n",
    "\n",
    "---\n",
    "\n",
    "> **Note:** <span>In this tutorial, we use a custom decorator to split class definitions across cells, making the code modular and allowing markdown explanations between methods for clarity. To add methods to a class, apply the custom decorator right before the method definition:</span>\n",
    "\n",
    "```python\n",
    "@add_to_class(ClassName)\n",
    "def method_name(self):\n",
    "    # Method implementation\n",
    "    pass\n",
    "```\n"
   ]
  },
  {
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     "start_time": "2025-01-31T01:28:16.026780Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def add_to_class(cls):\n",
    "    def decorator(func):\n",
    "        setattr(cls, func.__name__, func)\n",
    "        return func\n",
    "    return decorator"
   ],
   "id": "f27b8b894fb175b7",
   "outputs": [],
   "execution_count": 6
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "## Initialization of SimpleBuildingEnv\n",
    "\n",
    "1. **Inputs**:\n",
    "   - **`problem_specs`**: Defines system-specific properties such as state-space matrices, control input bounds, and dimensions.\n",
    "   - **`train_data`**: Provides sampled data for initial states, disturbances, and reference temperature bounds.\n",
    "   - **`data_stats`**: Contains mean and standard deviation values for normalizing the state observations.\n",
    "\n",
    "2. **Action Space**:\n",
    "   - A single scalar action (e.g., heating or cooling mass flow rate) is defined within the range `[0, 1]`, scaled appropriately using `problem_specs['umax']`.\n",
    "\n",
    "3. **Observation Space**:\n",
    "   - The observation includes the state of the system (`nx`), disturbances (`nd`), and reference temperature bounds (`nref`).\n",
    "   - It is represented as a continuous space with infinite bounds to accommodate varying inputs.\n",
    "\n",
    "\n"
   ],
   "id": "a1a4345d270bb031"
  },
  {
   "cell_type": "code",
   "id": "57b316ce0bdc9f63",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-31T01:28:16.239432Z",
     "start_time": "2025-01-31T01:28:16.072399Z"
    }
   },
   "source": [
    "import gym\n",
    "from gym import spaces"
   ],
   "outputs": [],
   "execution_count": 7
  },
  {
   "cell_type": "code",
   "id": "729880578dc7afff",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-31T01:28:16.248606Z",
     "start_time": "2025-01-31T01:28:16.245272Z"
    }
   },
   "source": [
    "class SimpleBuildingEnv(gym.Env):\n",
    "    \"\"\"Custom Environment for controlling the building indoor temperature using reinforcement learning.\"\"\"\n",
    "\n",
    "    def __init__(self, problem_specs, train_data, data_stats):\n",
    "        super().__init__()\n",
    "        \n",
    "        #Get the problem data and data statistics\n",
    "        self.state_means, self.state_stds = data_stats[\"means\"], data_stats[\"stds\"]\n",
    "        self.train_data = train_data\n",
    "        self.problem_specs = problem_specs\n",
    "        self.t = 0      #Initialize the timestep\n",
    "        self.idx = 0    #Initialize the batch index in training data\n",
    "\n",
    "        # Define action space: a single scalar action in [0, 1]\n",
    "        self.action_space = spaces.Box(\n",
    "            low=np.array([0.]),\n",
    "            high=np.array([1.]),\n",
    "            dtype=np.float32,\n",
    "            shape=(self.problem_specs['nu'],)\n",
    "        )\n",
    "\n",
    "        # Define observation space\n",
    "        self.observation_space = spaces.Box(\n",
    "            low=-np.inf,\n",
    "            high=np.inf,\n",
    "            dtype=np.float32,\n",
    "            shape=(\n",
    "                self.problem_specs['nx'] + self.problem_specs['nd'] + self.problem_specs['nref'] * 2,\n",
    "            )\n",
    "        )\n"
   ],
   "outputs": [],
   "execution_count": 8
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "#### Method: `reset`\n",
    "\n",
    "These methods provide essential functionality for resetting the environment and calculating feedback for the RL agent.\n",
    "\n",
    "The `reset` method initializes the environment at the start of an episode:\n",
    "1. **Initialization**: Selects the sample from `train_data` based on the current index for the initial state, disturbances, and reference bounds.\n",
    "2. **Reset Variables**: Resets the timestep (`t`) to 0 and `done` flag to `False`\n",
    "3. **Build State**: Constructs the normalized initial observation by calling the method `build_state()`.\n",
    "4. **Return**: Provides the initial observation.\n",
    "\n",
    "\n",
    "To add this method to the `SimpleBuildingEnv` class, we use the decorator `add_to_class(SimpleBuildingEnv)` \n",
    "\n"
   ],
   "id": "192eb8d207efee4e"
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   "cell_type": "code",
   "source": [
    "@add_to_class(SimpleBuildingEnv)\n",
    "def reset(self):\n",
    "    self.done = False\n",
    "    self.x = self.train_data[self.idx]['x']\n",
    "    self.y = self.x @ self.problem_specs['C'].T\n",
    "    self.d = self.train_data[self.idx]['d']\n",
    "    self.ymin = self.train_data[self.idx]['ymin']\n",
    "    self.ymax = self.train_data[self.idx]['ymax']\n",
    "    self.t = 0\n",
    "    obs = self.build_state()\n",
    "    return obs"
   ],
   "id": "b742a263776a37ad",
   "outputs": [],
   "execution_count": 9
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "#### Method: `build_state`\n",
    "\n",
    "The `build_state` method constructs the current state of the environment for the RL agent by combining various components of the system and normalizing them. This method ensures the agent receives a well-structured and scaled observation for training.\n",
    "\n",
    "#### Steps:\n",
    "1. **Combine Observations**:\n",
    "   - The method concatenates the following:\n",
    "     - Current system state (`self.x`)\n",
    "     - Reference temperature bounds (`self.ymin` and `self.ymax`)\n",
    "     - Current disturbance values (`self.d[self.t]`).\n",
    "\n",
    "2. **Normalize the State**:\n",
    "   - The combined observation is normalized using the mean and standard deviation values (`self.state_means`, `self.state_stds`) provided during initialization.\n"
   ],
   "id": "a14e6558c6a42637"
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  {
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   "cell_type": "code",
   "source": [
    "@add_to_class(SimpleBuildingEnv)\n",
    "def build_state(self):\n",
    "    combined_obs = torch.concat((self.x.squeeze(dim=0), self.ymin[self.t], self.ymax[self.t], self.d[self.t]))\n",
    "    # # Normalize the state\n",
    "    normalized_obs = (combined_obs - self.state_means) / self.state_stds\n",
    "    \n",
    "    return normalized_obs"
   ],
   "id": "c8e5142b629ce5d1",
   "outputs": [],
   "execution_count": 10
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "#### Methods `get_reward`, `get_cost`:\n",
    "The `get_reward` method computes the reward for the agent's action which is the energy consumption,  and the `get_cost` method penalizes deviations from the reference bounds (ymin, ymax).\n"
   ],
   "id": "9a4e8ea04bd87308"
  },
  {
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   },
   "cell_type": "code",
   "source": [
    "@add_to_class(SimpleBuildingEnv)\n",
    "def get_reward(self, action):\n",
    "    \"\"\"Calculate the reward based on the action.\"\"\"\n",
    "    reward = - (action * 0.01) * self.problem_specs['umax'].numpy()\n",
    "    return reward\n",
    "\n",
    "@add_to_class(SimpleBuildingEnv)\n",
    "def get_cost(self, output):\n",
    "    \"\"\"Calculate the cost based on the output.\"\"\"\n",
    "    cost = (np.maximum(self.ymin[self.t].numpy() - output.numpy(), 0) + np.maximum(output.numpy() - self.ymax[self.t].numpy(), 0)) * 50.0\n",
    "    return cost"
   ],
   "id": "e33182cb39e5a05",
   "outputs": [],
   "execution_count": 11
  },
  {
   "cell_type": "markdown",
   "id": "ccafc05718b4aa99",
   "metadata": {},
   "source": [
    "#### Method: `step`\n",
    "\n",
    "The `step` method updates the environment's state based on the agent's action and calculates feedback for learning.\n",
    "\n",
    "#### Steps:\n",
    "1. **Scale the Action**: Converts the agent's action to the appropriate scale using `umax`.\n",
    "2. **Simulate the Next State**: Updates the state (`x`) and output (`y`) using state-space equations.\n",
    "3. **Calculate Feedback**:\n",
    "   - **Reward**: `-1` * Energy consumption (`u`), due to the fact that RL agent aims to maximize the reward.\n",
    "   - **Cost**: Penalizes deviations from desired temperature bounds.\n",
    "4. **Advance Time**: Updates the timestep and handles data resets when the trajectory ends.\n",
    "5. **Return**: Provides the next state, reward, cost, and a `done` flag indicating whether the episode ends or not.\n",
    "\n",
    "> **Note:** <span>This part of the code is for checking if the episode ends or not, and transfering to the next batch of training data:</span>\n",
    "\n",
    "```python\n",
    "if self.t == len(self.train_data[0]['d']):\n",
    "        done = True\n",
    "        self.idx = (self.idx+1)%len(self.train_data)\n",
    "        self.d = self.train_data[self.idx]['d']\n",
    "        self.t = 0\n",
    "```\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "id": "919e986ce7c34b0c",
   "metadata": {
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   "source": [
    "@add_to_class(SimpleBuildingEnv)\n",
    "def step(self, action):\n",
    "    \"\"\"Take a step in the environment by the given action. Returns the next state, reward, cost. \"\"\"\n",
    "    \n",
    "    # Scale the action\n",
    "    scaled_action = torch.tensor(action) * self.problem_specs['umax']\n",
    "    scaled_action = scaled_action.to(torch.float32)\n",
    "\n",
    "    # Simulate the next state\n",
    "    self.x = self.x @ self.problem_specs[\"A\"].T + scaled_action @ self.problem_specs[\"B\"].T + self.d[self.t] @ self.problem_specs[\"E\"].T\n",
    "    self.y = self.x @ self.problem_specs[\"C\"].T\n",
    "\n",
    "    # Calculate reward and cost\n",
    "    reward = self.get_reward(action)\n",
    "    cost = self.get_cost(self.y)\n",
    "    \n",
    "    # Update the timestep \n",
    "    self.t += 1\n",
    "    \n",
    "    done = False\n",
    "    \n",
    "    if self.t == len(self.d):\n",
    "        done = True\n",
    "        self.idx = (self.idx+1)%len(self.train_data)\n",
    "        self.d = self.train_data[self.idx]['d']\n",
    "        self.t = 0\n",
    "        \n",
    "    next_obs = self.build_state()\n",
    "    \n",
    "    return next_obs, reward, cost, done\n"
   ],
   "outputs": [],
   "execution_count": 12
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "#### Method `inference()`:\n",
    "\n",
    "The `inference` method uses a trained policy and simulates its performance in the environment for a continuous trajectory and logs key metrics.\n",
    "\n",
    "#### Steps:\n",
    "\n",
    "1. **Initialization**:\n",
    "   - Reset the environment state with `inf_data`.\n",
    "   - Initialize logs for rewards, costs, actions, outputs, and observations.\n",
    "\n",
    "2. **Simulation Loop**:\n",
    "   - Iterate over the length of the disturbance signal.\n",
    "   - Select actions using the policy.\n",
    "   - Step through the environment with `env.step(action)`.\n",
    "   - Store rewards, costs, actions, outputs, and observations.\n",
    "\n",
    "3. **Return Metrics**:\n",
    "   - Outputs a dictionary containing:\n",
    "     - `\"y\"`: System outputs.\n",
    "     - `\"rewards\"`: Reward at each step.\n",
    "     - `\"costs\"`: Constraint violations at each step.\n",
    "     - `\"actions\"`: Actions taken at each step.\n",
    "     - `\"obs_hist\"`: State observations.\n"
   ],
   "id": "dcbfc03439a3b320"
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  {
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   "source": [
    "@add_to_class(SimpleBuildingEnv)\n",
    "def inference(self, inf_data, policy):\n",
    "    \"\"\"\n",
    "    Perform inference using a trained policy on the environment.\n",
    "\n",
    "    Args:\n",
    "        inf_data (dict): Dictionary containing initial conditions and reference data.\n",
    "        policy: Trained agent policy.\n",
    "\n",
    "    Returns:\n",
    "        dict: Contains history of rewards, costs, actions, outputs, and observations.\n",
    "    \"\"\"\n",
    "\n",
    "    # Initialize logs\n",
    "    eval_hist_rewards = []\n",
    "    eval_hist_cost = []\n",
    "    eval_hist_actions = []\n",
    "    eval_hist_outputs = []\n",
    "    eval_hist_ymin = []\n",
    "    eval_hist_ymax = []\n",
    "    obs_hist = []\n",
    "\n",
    "    # Reset environment for inference\n",
    "    self.t = 0\n",
    "    self.x = inf_data['x'].reshape(self.x.shape)\n",
    "    self.y = self.x @ self.problem_specs[\"C\"].T\n",
    "    self.d = inf_data['d'].squeeze(dim=0)\n",
    "    self.ymin = inf_data['ymin'][0]\n",
    "    self.ymax = inf_data['ymax'][0]\n",
    "    obs = self.build_state()\n",
    "\n",
    "    # Log initial conditions\n",
    "    eval_hist_ymin.append(self.ymin)\n",
    "    eval_hist_ymax.append(self.ymax)\n",
    "    eval_hist_outputs.append(self.y)\n",
    "\n",
    "    # Rollout simulation using env.step()\n",
    "    while self.t != len(self.d) - 1:\n",
    "        \n",
    "        # Select an action using the policy\n",
    "        action = policy.select_action(obs)\n",
    "\n",
    "        # Take a step in the environment\n",
    "        next_obs, reward, cost, done = self.step(action)\n",
    "\n",
    "        # Log step details\n",
    "        eval_hist_rewards.append(reward)\n",
    "        eval_hist_cost.append(cost)\n",
    "        eval_hist_actions.append(action * self.problem_specs['umax'].numpy())\n",
    "        eval_hist_outputs.append(self.y)\n",
    "        denormalized_obs = next_obs.clone().detach() * self.state_stds + self.state_means\n",
    "        obs_hist.append(denormalized_obs)\n",
    "\n",
    "        # Update the observation\n",
    "        obs = next_obs\n",
    "\n",
    "    # Pack results into a dictionary\n",
    "    outputs = {\n",
    "        \"y\": eval_hist_outputs,\n",
    "        \"rewards\": eval_hist_rewards,\n",
    "        \"costs\": eval_hist_cost,\n",
    "        \"u\": eval_hist_actions,\n",
    "        \"obs_hist\": obs_hist\n",
    "    }\n",
    "    return outputs"
   ],
   "id": "705a538ac6a5de8d",
   "outputs": [],
   "execution_count": 13
  },
  {
   "cell_type": "markdown",
   "id": "78ce3a9c9d319818",
   "metadata": {},
   "source": [
    "### Example on initializing the environment and run simulation:\n",
    "```python\n",
    "#to initialize:\n",
    "env = SimpleBuildingEnv(problem_specs, data[\"train_data\"], data_stats)\n",
    "```\n",
    "\n",
    "In the learning process, the action comes from the policy network, however, `gym` objects have a built-in method for sampling the action space. as an example:\n",
    "```python\n",
    "env.reset()   #resets the environment and returns the initial observation of the agent\n",
    "env.step(env.action_space.sample())   #takes a step forward with a randomly sampled action\n",
    "``` "
   ]
  },
  {
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     "start_time": "2025-01-31T01:28:16.523667Z"
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   "cell_type": "code",
   "outputs": [],
   "source": [
    "nsteps=100\n",
    "n_samples=1000\n",
    "x_min=18.0\n",
    "x_max=22.0\n",
    "ref_range=2.0\n",
    "batch_size=100\n",
    "problem_specs, data_stats, data, data_loader = generate_data(nsteps, n_samples, x_min, x_max, ref_range, batch_size)\n"
   ],
   "id": "a528b4e8261fbde5",
   "execution_count": null
  },
  {
   "metadata": {
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     "end_time": "2025-01-31T01:28:16.755731Z",
     "start_time": "2025-01-31T01:28:16.752211Z"
    }
   },
   "cell_type": "code",
   "source": [
    "train_env = SimpleBuildingEnv(problem_specs, data[\"train_data\"], data_stats)\n",
    "dev_env = SimpleBuildingEnv(problem_specs, data[\"dev_data\"], data_stats)"
   ],
   "id": "deb3f21a1ae28ee8",
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/alireza/anaconda3/envs/neuromancer/lib/python3.10/site-packages/gym/spaces/box.py:127: UserWarning: \u001B[33mWARN: Box bound precision lowered by casting to float32\u001B[0m\n",
      "  logger.warn(f\"Box bound precision lowered by casting to {self.dtype}\")\n"
     ]
    }
   ],
   "execution_count": 15
  },
  {
   "cell_type": "markdown",
   "id": "ba692f7bc57d6410",
   "metadata": {},
   "source": [
    "### Class: `ReplayBuffer`\n",
    "\n",
    "The `ReplayBuffer` class is essential for RL off-policy training as it allows the agent to store and reuse past experiences, enabling stable and efficient training. By sampling random batches of transitions, it breaks the correlation between consecutive experiences, improving the training of deep RL algorithms.\n",
    "\n",
    "#### Methods:\n",
    "1. **`__init__(self, max_size=1e6)`**:\n",
    "   - Initializes the buffer with a maximum size using a deque.\n",
    "\n",
    "2. **`add(self, transition)`**:\n",
    "   - Adds a single transition `(state, next_state, action, reward, cost, done)` to the buffer.\n",
    "\n",
    "3. **`sample(self, batch_size)`**:\n",
    "   - Randomly samples a batch of transitions and returns tensors for states, next states, actions, rewards, costs, and done flags.\n",
    "\n",
    "This replay buffer ensures that the RL agent learns effectively from diverse and decorrelated experiences, which is crucial for generalizing to various scenarios.\n",
    "\n",
    "> **Note:** \n",
    "The concept of experience replay for RL agents was first introduced in this paper:\n",
    "*Playing Atari with Deep Reinforcement Learning* by Mnih et al., 2013 ([arXiv:1312.5602](https://arxiv.org/abs/1312.5602))."
   ]
  },
  {
   "cell_type": "code",
   "id": "872ee3d04e2b06c4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-31T01:28:16.850904Z",
     "start_time": "2025-01-31T01:28:16.846532Z"
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   },
   "source": [
    "class ReplayBuffer:\n",
    "    def __init__(self, max_size=1e6):\n",
    "        \"\"\"\n",
    "        Initialize the replay buffer.\n",
    "\n",
    "        Args:\n",
    "            max_size (int): Maximum number of transitions to store in the buffer.\n",
    "        \"\"\"\n",
    "        self.storage = deque(maxlen=int(max_size))\n",
    "\n",
    "    def add(self, transition):\n",
    "        \"\"\"\n",
    "        Add a transition to the replay buffer.\n",
    "\n",
    "        Args:\n",
    "            transition (tuple): A tuple containing (state, next_state, action, reward, cost, done).\n",
    "        \"\"\"\n",
    "        self.storage.append(transition)\n",
    "\n",
    "    def sample(self, batch_size):\n",
    "        \"\"\"\n",
    "        Sample a batch of transitions from the replay buffer.\n",
    "\n",
    "        Args:\n",
    "            batch_size (int): Number of transitions to sample.\n",
    "\n",
    "        Returns:\n",
    "            tuple: Batch of (states, next_states, actions, rewards, costs, dones).\n",
    "        \"\"\"\n",
    "        indices = torch.randint(0, len(self.storage), (batch_size,))\n",
    "        batch_states, batch_next_states, batch_actions, batch_rewards, batch_costs, batch_dones = [], [], [], [], [], []\n",
    "\n",
    "        for i in indices:\n",
    "            state, next_state, action, reward, cost, done= self.storage[i]\n",
    "            batch_states.append(state)\n",
    "            batch_next_states.append(next_state)\n",
    "            batch_actions.append(action)\n",
    "            batch_rewards.append(reward)\n",
    "            batch_costs.append(cost)\n",
    "            batch_dones.append(done)\n",
    "\n",
    "        return (\n",
    "            torch.stack(batch_states),\n",
    "            torch.stack(batch_next_states),\n",
    "            torch.stack(batch_actions),\n",
    "            torch.tensor(batch_rewards, dtype=torch.float32).view(-1, 1),\n",
    "            torch.tensor(batch_costs, dtype=torch.float32).view(-1, 1),\n",
    "            torch.tensor(batch_dones, dtype=torch.float32).view(-1, 1),\n",
    "\n",
    "        )\n"
   ],
   "outputs": [],
   "execution_count": 17
  },
  {
   "cell_type": "markdown",
   "id": "1a7a0a150b69cf5a",
   "metadata": {},
   "source": [
    "## Primal-Dual Deep Deterministic Policy Gradient (PD-DDPG)\n",
    "\n",
    "PD-DDPG is a reinforcement learning algorithm tailored for **Constrained Markov Decision Processes (CMDPs)**, where the agent must balance maximizing rewards while satisfying constraints. It builds upon the [Deep Deterministic Policy Gradient (DDPG)](https://arxiv.org/abs/1509.02971) method by introducing a **primal-dual optimization framework** to handle constraints effectively. In this tutorial, we set the discount factor ($\\gamma$) for the cost network to 0, focusing on immediate constraint satisfaction, while the reward network retains a positive discount factor to encourage long-term optimization.\n",
    "\n",
    "---\n",
    "\n",
    "### PD-DDPG Framework\n",
    "\n",
    "#### **State Transition Dynamics**\n",
    "The environment is modeled as a Markov Decision Process (MDP) with the dynamics:\n",
    "$$\n",
    "s_{t+1} = f(s_t, a_t) + \\epsilon_t\n",
    "$$\n",
    "where:\n",
    "- $s_t$: State at time $t$ (e.g., temperature and disturbances),\n",
    "- $a_t$: Action taken (e.g., HVAC adjustment),\n",
    "- $f$: State transition function, which is learned by interacting with the environment.\n",
    "- $\\epsilon_t$: Noise (in stochastic environments)\n",
    "\n",
    "The agent learns to transition through states to achieve optimal performance while minimizing constraint violations.\n",
    "\n",
    "---\n",
    "\n",
    "#### **Primal-Dual Optimization**\n",
    "The algorithm optimizes the Lagrangian function:\n",
    "$$\n",
    "\\mathcal{L}(\\pi, \\lambda) = \\mathbb{E}_{\\pi} \\left[ r(s_t, a_t) - \\lambda \\cdot c(s_t, a_t) \\right]\n",
    "$$\n",
    "where:\n",
    "- $r(s_t, a_t)$: Reward for energy efficiency,\n",
    "- $c(s_t, a_t)$: Cost for constraint violations (e.g., exceeding temperature bounds),\n",
    "- $\\lambda$: Dual variable balancing rewards and costs,\n",
    "- $\\pi(a_t | s_t)$: Policy guiding the agent’s actions.\n",
    "\n",
    "This dual approach dynamically adjusts the importance of constraint satisfaction versus reward maximization.\n",
    "\n",
    "---\n",
    "\n",
    "### Networks in PD-DDPG\n",
    "\n",
    "#### Actor Network\n",
    "The **actor network** generates actions based on the current state, aiming to maximize rewards and minimize costs. It outputs continuous actions scaled to the environment's limits (e.g., HVAC adjustments). Smooth action transitions are encouraged by penalizing abrupt changes between consecutive actions.\n",
    "\n",
    "#### Critic Network\n",
    "The **critic network** evaluates the chosen actions by estimating the expected cumulative reward:\n",
    "$$\n",
    "Q(s_t, a_t) = \\mathbb{E} \\left[ r(s_t, a_t) + \\gamma \\cdot Q(s_{t+1}, a_{t+1}) \\right]\n",
    "$$\n",
    "This helps the actor learn which actions lead to higher rewards over time.\n",
    "\n",
    "#### Cost Network\n",
    "The **cost network** directly estimates the immediate cost:\n",
    "$$\n",
    "C(s_t, a_t) = c(s_t, a_t)\n",
    "$$\n",
    "By setting $\\gamma = 0$ for the cost network, the algorithm prioritizes real-time constraint satisfaction.\n",
    "\n"
   ]
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "### Actor Network\n",
    "\n",
    "The **Actor Network** generates continuous actions for the agent based on the current state. It uses multiple fully connected layers with customizable activation functions and a final output layer (e.g., Sigmoid in this case to have the scaled action between `[0,1]` ) to scale actions within a specified range.\n",
    "\n",
    "#### Creating an Actor Object\n",
    "\n",
    "To create an instance of the `Actor` class, specify the dimensions and hidden layers:\n",
    "\n",
    "```python\n",
    "state_dim = 10  # Example state dimension\n",
    "action_dim = 2  # Example action dimension\n",
    "hidden_layers = [64, 64]  # Two hidden layers with 64 neurons each\n",
    "\n",
    "actor = Actor(state_dim, action_dim, hidden_layers)\n",
    "```"
   ],
   "id": "ec9f742e056615e3"
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  {
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   "cell_type": "code",
   "source": [
    "# Actor Network\n",
    "class Actor(nn.Module):\n",
    "    def __init__(self, state_dim, action_dim, hidden_layers, activation=nn.ReLU, output_activation=nn.Sigmoid):\n",
    "        super(Actor, self).__init__()\n",
    "        layers = []\n",
    "        input_dim = state_dim\n",
    "        for hidden_dim in hidden_layers:\n",
    "            layers.append(nn.Linear(input_dim, hidden_dim))\n",
    "            if activation:\n",
    "                layers.append(activation())\n",
    "            input_dim = hidden_dim\n",
    "        layers.append(nn.Linear(input_dim, action_dim))\n",
    "        if output_activation:\n",
    "            layers.append(output_activation())\n",
    "        self.model = nn.Sequential(*layers)\n",
    "\n",
    "    def forward(self, state):\n",
    "        return self.model(state)\n"
   ],
   "id": "268572e684f58010",
   "outputs": [],
   "execution_count": 18
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "### Critic or Cost Network\n",
    "\n",
    "The **Critic Network** evaluates the quality of an action in a given state by estimating the Q-value. Similarly, the **Cost Network** shares the same architecture but focuses on estimating the immediate cost associated with an action. Both networks take the concatenated state and action as input, pass them through hidden layers with activation functions, and output a single scalar value (Q-value or C-value).\n",
    "\n",
    "#### Creating a Critic or Cost Network\n",
    "\n",
    "To instantiate either a `Critic` or a `Cost` network, specify the state and action dimensions along with the hidden layers:\n",
    "\n",
    "```python\n",
    "state_dim = 10  # Example state dimension\n",
    "action_dim = 2  # Example action dimension\n",
    "hidden_layers = [128, 128]  # Two hidden layers with 128 neurons each\n",
    "\n",
    "critic_net = Critic(state_dim, action_dim, hidden_layers)\n",
    "cost_net = Critic(state_dim, action_dim, hidden_layers)  # Same architecture for the Cost Network\n",
    "```"
   ],
   "id": "ed5dcc1e107b9145"
  },
  {
   "metadata": {
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     "end_time": "2025-01-31T01:28:16.953384Z",
     "start_time": "2025-01-31T01:28:16.949956Z"
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   "cell_type": "code",
   "source": [
    "class Critic(nn.Module):\n",
    "    def __init__(self, state_dim, action_dim, hidden_layers, activation=nn.ReLU):\n",
    "\n",
    "        super(Critic, self).__init__()\n",
    "        layers = []\n",
    "        input_dim = state_dim + action_dim\n",
    "\n",
    "        # Create hidden layers with activation functions\n",
    "        for hidden_dim in hidden_layers:\n",
    "            layers.append(nn.Linear(input_dim, hidden_dim))\n",
    "            layers.append(activation())  # Add activation after each layer\n",
    "            input_dim = hidden_dim\n",
    "\n",
    "        # Output layer (no activation here)\n",
    "        layers.append(nn.Linear(input_dim, 1))\n",
    "        self.layers = nn.Sequential(*layers)\n",
    "\n",
    "    def forward(self, state, action):\n",
    "\n",
    "        x = torch.cat([state, action], dim=-1)  # Concatenate state and action\n",
    "        return self.layers(x)"
   ],
   "id": "909fa190ad12433b",
   "outputs": [],
   "execution_count": 19
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "### PD-DDPG Agent Initialization\n",
    "\n",
    "The **PD-DDPG** class sets up the actor, critic, and cost networks along with their target networks. It initializes the dual variable ($\\lambda$) and handles hyperparameters like learning rates, constraint limits, and network architectures.\n"
   ],
   "id": "c522cbaa8b3dfa7e"
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   },
   "cell_type": "code",
   "source": [
    "class PD_DDPG:\n",
    "    def __init__(self, state_dim, action_dim, max_action, lambda_, lambda_step, constraint_limit, lr_critic, lr_actor, hidden_layers):\n",
    "        \"\"\"\n",
    "        Initialize the PD-DDPG agent.\n",
    "\n",
    "        Args:\n",
    "            state_dim (int): Dimension of state space.\n",
    "            action_dim (int): Dimension of state space.\n",
    "            max_action (float): Maximum action value.\n",
    "            lambda_ (float): Initial dual variable for the constraint.\n",
    "            lambda_step (float): Step size for updating lambda.\n",
    "            constraint_limit (float): Constraint tolerance limit.\n",
    "            lr_critic (float): Learning rate for the critic.\n",
    "            lr_actor (float): Learning rate for the actor.\n",
    "            hidden_layers (list): List of integers specifying the number of neurons in each hidden layer.\n",
    "        \"\"\"\n",
    "\n",
    "        # Actor network and optimizer\n",
    "        self.actor = Actor(state_dim, action_dim, hidden_layers).to(device)\n",
    "        self.actor_target = Actor(state_dim, action_dim, hidden_layers).to(device)\n",
    "        self.actor_target.load_state_dict(self.actor.state_dict())\n",
    "        self.actor_optimizer = torch.optim.Adam(self.actor.parameters(), lr=lr_actor)\n",
    "\n",
    "        # Critic network and optimizer\n",
    "        self.critic = Critic(state_dim, action_dim, hidden_layers).to(device)\n",
    "        self.critic_target = Critic(state_dim, action_dim, hidden_layers).to(device)\n",
    "        self.critic_target.load_state_dict(self.critic.state_dict())\n",
    "        self.critic_optimizer = torch.optim.Adam(self.critic.parameters(), lr=lr_critic)\n",
    "\n",
    "        # Cost network and optimizer\n",
    "        self.cost = Critic(state_dim, action_dim, hidden_layers).to(device)\n",
    "        self.cost_target = Critic(state_dim, action_dim, hidden_layers).to(device)\n",
    "        self.cost_target.load_state_dict(self.cost.state_dict())\n",
    "        self.cost_optimizer = torch.optim.Adam(self.cost.parameters(), lr=lr_critic)\n",
    "\n",
    "        # Lambda and constraint\n",
    "        self.lambda_ = torch.tensor([lambda_], requires_grad=False).to(device)\n",
    "        self.lambda_step = lambda_step\n",
    "        self.constraint_limit = constraint_limit\n",
    "\n",
    "        self.max_action = max_action\n"
   ],
   "id": "769242cefa748645",
   "outputs": [],
   "execution_count": 20
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "### Transition Storage and Action Selection\n",
    "\n",
    "- **`store_transition`**: Normalizes and stores a state-action transition in the replay buffer for training.\n",
    "- **`select_action`**: Uses the actor network to compute and return a clipped action for a given state.\n",
    "\n",
    "> **Note:** <span>To add these methods, we use the decorator `add_to_class(PD-DDPG)`:</span>\n"
   ],
   "id": "7ebec9db8ef25601"
  },
  {
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   "cell_type": "code",
   "source": [
    "@add_to_class(PD_DDPG)\n",
    "def store_transition(self, replay_buffer, state, next_state, action, reward, cost, done):\n",
    "    \"\"\"\n",
    "    Normalizing the transition and storing it in the replay buffer.\n",
    "\n",
    "    \"\"\"\n",
    "    # Update the normalizer\n",
    "    state = state.clone().detach().to(torch.float32).to(device)\n",
    "    next_state = next_state.clone().detach().to(torch.float32).to(device)\n",
    "    action = torch.tensor(action, dtype=torch.float32).to(device)\n",
    "    reward = torch.tensor(reward, dtype=torch.float32).to(device)\n",
    "    cost = torch.tensor(cost, dtype=torch.float32).to(device)\n",
    "    done = torch.tensor(done, dtype=torch.float32).to(device)\n",
    "\n",
    "\n",
    "    # Store in the replay buffer\n",
    "    replay_buffer.add((state, next_state, action, reward, cost, done))\n",
    "\n",
    "@add_to_class(PD_DDPG)\n",
    "def select_action(self, state):\n",
    "    \"\"\"\n",
    "    Select an action for a given state.\n",
    "\n",
    "    Args:\n",
    "        state (np.ndarray or list): Current state.\n",
    "\n",
    "    Returns:\n",
    "        np.ndarray: Action.\n",
    "    \"\"\"\n",
    "    # Ensure the state is a tensor\n",
    "    if isinstance(state, list):\n",
    "        state = torch.tensor(state, dtype=torch.float32)\n",
    "    elif isinstance(state, np.ndarray):\n",
    "        state = torch.tensor(state, dtype=torch.float32)\n",
    "\n",
    "    state = state.to(device).unsqueeze(0)  # Add batch dimension\n",
    "    action = self.actor(state).cpu().data.numpy().reshape(1, 1) * self.max_action\n",
    "    return np.clip(action, 0, self.max_action)\n"
   ],
   "id": "1f010109229c0b19",
   "outputs": [],
   "execution_count": 21
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "### Training the PD-DDPG Agent\n",
    "> **Note:** \n",
    "It is highly recommended to read this paper to fully understand the algorithm:\n",
    "*Continuous control with deep reinforcement learning* by Lillicrap et al., 2015 (https://arxiv.org/abs/1509.02971)).\n",
    "\n",
    "The networks are trained using the following process:\n",
    "\n",
    "1. **Sampling Transitions**: Transitions $(s_t, a_t, r_t, c_t, s_{t+1})$ are sampled from the replay buffer, representing the agent's experiences in the environment.\n",
    "\n",
    "2. **Critic Update**: The critic minimizes the error between the estimated and target Q-values:\n",
    "   $$\n",
    "   \\mathcal{L}_{\\text{critic}} = \\mathbb{E} \\left[ \\left( Q(s_t, a_t) - \\left( r(s_t, a_t) + \\gamma \\cdot Q(s_{t+1}, a_{t+1}) \\right) \\right)^2 \\right]\n",
    "   $$\n",
    "   Here:\n",
    "   - **Target Q-value**: Computed using the **critic target network**, which evaluates the next state ($s_{t+1}$) and next action ($a_{t+1}$).\n",
    "   - **next action ($a_{t+1}$)**: Generated by the **actor target network**, ensuring smoother updates and preventing instability.\n",
    "   - In the code, the loss is implemented as:\n",
    "      - ```python\n",
    "        next_action = self.actor_target(next_state)\n",
    "        target_Q = self.critic_target(next_state, next_action)\n",
    "        target_Q = reward + ((1 - done) * discount * target_Q).detach()\n",
    "         \n",
    "        # Update critic\n",
    "        current_Q = self.critic(state, action)\n",
    "        critic_loss = F.mse_loss(current_Q, target_Q)\n",
    "        self.critic_optimizer.zero_grad()\n",
    "        critic_loss.backward()\n",
    "        self.critic_optimizer.step()\n",
    "        ```\n",
    "   \n",
    "    \n",
    "\n",
    "3. **Cost Update**: The cost network minimizes the error in cost estimation:\n",
    "   $$\n",
    "   \\mathcal{L}_{\\text{cost}} = \\mathbb{E} \\left[ \\left( C(s_t, a_t) - c(s_t, a_t) \\right)^2 \\right]\n",
    "   $$\n",
    "   Similar to the critic, the cost network also uses the **target network** architecture to estimate costs.\n",
    "   - In the code, the loss is implemented as:\n",
    "      - ```python\n",
    "        target_C = self.cost_target(next_state, next_action)\n",
    "        #Update cost network\n",
    "        current_C = self.cost(state, action)\n",
    "        cost_loss = F.mse_loss(current_C, target_C)\n",
    "        self.cost_optimizer.zero_grad()\n",
    "        cost_loss.backward()\n",
    "        self.cost_optimizer.step()\n",
    "        ```\n",
    "    \n",
    "\n",
    "4. **Actor Update**: The actor is trained to maximize rewards and minimize costs:\n",
    "   $$\n",
    "   \\mathcal{L}_{\\text{actor}} = -\\mathbb{E} \\left[ Q(s_t, a_t) - \\lambda \\cdot C(s_t, a_t) \\right]\n",
    "   $$\n",
    "   - The actor learns by updating actions based on feedback from the critic (rewards) and cost networks (constraints).\n",
    "   - Smooth actions are encouraged by penalizing large changes between consecutive actions:\n",
    "     $$\n",
    "     \\mathcal{L}_{\\text{actor}} += \\beta \\cdot \\| a_t - a_{t+1} \\|^2\n",
    "     $$\n",
    "       - In the code, the loss is implemented as:\n",
    "            ```python\n",
    "            actor_loss = -(Q_value - self.lambda_.detach() * C_value).mean() + nn.MSELoss()(self.actor(state), self.actor_target(next_state))\n",
    "            ```\n",
    "\n",
    "5. **Dual Variable Update**: The dual variable $\\lambda$ is updated dynamically to balance the trade-off between rewards and costs. This ensures the agent adheres to constraints while optimizing its policy:\n",
    "\n",
    "    $$\n",
    "    \\lambda \\leftarrow \\max(0, \\lambda + \\alpha \\cdot (C(s_t, a_t) - d))\n",
    "    $$\n",
    "    \n",
    "    Here:\n",
    "    - **$\\alpha$**: The step size for updating $\\lambda$.\n",
    "    - **$C(s_t, a_t)$**: The immediate cost incurred.\n",
    "    - **$d$**: The acceptable cost threshold.\n",
    "    - code:\n",
    "        ```python\n",
    "       #Update lambda\n",
    "        lambda_gradient = (self.cost(state, self.actor(state)) - (self.constraint_limit)).mean()\n",
    "        self.lambda_ = torch.clamp(self.lambda_ + self.lambda_step * lambda_gradient, min=0).detach()\n",
    "        ```\n",
    "    \n",
    "    When constraints are violated, $\\lambda$ increases to prioritize cost minimization. Conversely, when the policy satisfies constraints, $\\lambda$ decreases.\n",
    "\n",
    "6. **Target Network Soft Updates**: To ensure training stability, the target networks for the critic, cost, and actor are updated using soft updates:\n",
    "    \n",
    "    $$\n",
    "    \\theta_{\\text{target}} \\leftarrow \\tau \\theta_{\\text{online}} + (1 - \\tau) \\theta_{\\text{target}}\n",
    "    $$\n",
    "    \n",
    "    Where:\n",
    "    - **$\\tau \\in [0, 1]$**: The soft update coefficient determining the blending of the target and online network parameters.\n",
    "    \n",
    "    This approach maintains a stable target for training while allowing gradual adaptation to changes in the policy.\n",
    "    - in the code section:  \n",
    "       ```python\n",
    "       # Soft update target networks\n",
    "        for param, target_param in zip(self.actor.parameters(), self.actor_target.parameters()):\n",
    "            target_param.data.copy_(tau * param.data + (1 - tau) * target_param.data)\n",
    "\n",
    "        for param, target_param in zip(self.critic.parameters(), self.critic_target.parameters()):\n",
    "            target_param.data.copy_(tau * param.data + (1 - tau) * target_param.data)\n",
    "\n",
    "        for param, target_param in zip(self.cost.parameters(), self.cost_target.parameters()):\n",
    "            target_param.data.copy_(tau * param.data + (1 - tau) * target_param.data)\n",
    "       ```\n",
    "      \n",
    "\n",
    "#### Inputs to the `train` Function\n",
    "\n",
    "- **`replay_buffer`**: Transitions storage.\n",
    "- **`iterations`**: Number of training steps to perform.\n",
    "- **`batch_size`** *(default: 100)*: Number of transitions sampled from the replay buffer per training step.\n",
    "- **`discount`** *(default: 0.99)*: Discount factor ($\\gamma$) for future rewards in the critic update.\n",
    "- **`tau`** *(default: 0.005)*: Soft update coefficient for target networks.\n",
    "- **`policy_freq`** *(default: 2)*: Frequency of actor and target network updates relative to critic updates.\n",
    "\n"
   ],
   "id": "f9bc30b8276a7d79"
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  {
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   "id": "b3777862817f453d",
   "metadata": {
    "ExecuteTime": {
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   "source": [
    "@add_to_class(PD_DDPG)\n",
    "def train(self, replay_buffer, iterations, batch_size=100, discount=0.99, tau=0.005, policy_freq=2):\n",
    "    \"\"\"\n",
    "    Train the PD-DDPG agent.\n",
    "\n",
    "    Args:\n",
    "        replay_buffer (ReplayBuffer): Replay buffer.\n",
    "        iterations (int): Number of training iterations.\n",
    "        batch_size (int): Batch size.\n",
    "        discount (float): Discount factor.\n",
    "        tau (float): Soft update coefficient.\n",
    "        policy_freq (int): Frequency of policy updates.\n",
    "    \"\"\"\n",
    "    for iteration in range(iterations):\n",
    "        # Sample batch of transitions\n",
    "        (\n",
    "            batch_states,\n",
    "            batch_next_states,\n",
    "            batch_actions,\n",
    "            batch_rewards,\n",
    "            batch_costs,\n",
    "            batch_dones,\n",
    "        ) = replay_buffer.sample(batch_size)\n",
    "\n",
    "        # Convert to tensors\n",
    "        state = batch_states.clone().detach().to(torch.float32).to(device)\n",
    "        next_state = batch_next_states.clone().detach().to(torch.float32).to(device)\n",
    "        action = batch_actions.clone().detach().to(torch.float32).to(device)\n",
    "        reward = batch_rewards.clone().detach().to(torch.float32).to(device)\n",
    "        cost = batch_costs.clone().detach().to(torch.float32).to(device)\n",
    "        done = batch_dones.clone().detach().to(torch.float32).to(device)\n",
    "        \n",
    "        \n",
    "\n",
    "        # Compute target values\n",
    "        next_action = self.actor_target(next_state)\n",
    "        target_Q = reward + (1 - done) * discount * self.critic_target(next_state, next_action).detach()\n",
    "        target_C = cost\n",
    "\n",
    "        # Update critic\n",
    "        current_Q = self.critic(state, action)\n",
    "        critic_loss = F.mse_loss(current_Q, target_Q)\n",
    "        self.critic_optimizer.zero_grad()\n",
    "        critic_loss.backward()\n",
    "        self.critic_optimizer.step()\n",
    "\n",
    "        # Update cost network\n",
    "        current_C = self.cost(state, action)\n",
    "        cost_loss = F.mse_loss(current_C, target_C)\n",
    "        self.cost_optimizer.zero_grad()\n",
    "        cost_loss.backward()\n",
    "        self.cost_optimizer.step()\n",
    "\n",
    "        # Delayed policy updates\n",
    "        if iteration % policy_freq == 0:\n",
    "            # Update actor network\n",
    "            Q_value = self.critic(state, self.actor(state))\n",
    "            C_value = self.cost(state, self.actor(state))\n",
    "            actor_loss = -(\n",
    "                Q_value - self.lambda_.detach() * C_value\n",
    "            ).mean() + F.mse_loss(self.actor(state), self.actor_target(next_state))\n",
    "            self.actor_optimizer.zero_grad()\n",
    "            actor_loss.backward()\n",
    "            self.actor_optimizer.step()\n",
    "\n",
    "            # Soft update target networks\n",
    "            for param, target_param in zip(self.actor.parameters(), self.actor_target.parameters()):\n",
    "                target_param.data.copy_(tau * param.data + (1 - tau) * target_param.data)\n",
    "\n",
    "            for param, target_param in zip(self.critic.parameters(), self.critic_target.parameters()):\n",
    "                target_param.data.copy_(tau * param.data + (1 - tau) * target_param.data)\n",
    "\n",
    "            for param, target_param in zip(self.cost.parameters(), self.cost_target.parameters()):\n",
    "                target_param.data.copy_(tau * param.data + (1 - tau) * target_param.data)\n",
    "\n",
    "            # Update lambda\n",
    "            lambda_gradient = (self.cost(state, self.actor(state)) - self.constraint_limit).mean()\n",
    "            self.lambda_ = torch.clamp(self.lambda_ + self.lambda_step * lambda_gradient, min=0).detach()\n"
   ],
   "outputs": [],
   "execution_count": 22
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "#### Model Save and Load\n",
    "\n",
    "- **`save`**: Saves the weights of the actor, critic, and cost networks to the specified directory.\n",
    "- **`load`**: Loads the saved weights for the actor, critic, and cost networks from the specified directory.\n"
   ],
   "id": "a3fb0a9d9ccd9252"
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  {
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   "source": [
    "@add_to_class(PD_DDPG)\n",
    "def save(self, filename, directory):\n",
    "    \"\"\"\n",
    "    Save model weights.\n",
    "    \"\"\"\n",
    "    torch.save(self.actor.state_dict(), f'{directory}/{filename}_actor.pth')\n",
    "    torch.save(self.critic.state_dict(), f'{directory}/{filename}_critic.pth')\n",
    "    torch.save(self.cost.state_dict(), f'{directory}/{filename}_cost.pth')\n",
    "\n",
    "@add_to_class(PD_DDPG)\n",
    "def load(self, filename, directory):\n",
    "    \"\"\"\n",
    "    Load model weights.\n",
    "    \"\"\"\n",
    "    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "    load_path = lambda model_type: torch.load(f'{directory}/{filename}_{model_type}.pth', map_location=device)\n",
    "\n",
    "    self.actor.load_state_dict(load_path('actor'))\n",
    "    self.critic.load_state_dict(load_path('critic'))\n",
    "    self.cost.load_state_dict(load_path('cost'))"
   ],
   "id": "f83d3581dede8688",
   "outputs": [],
   "execution_count": 23
  },
  {
   "cell_type": "markdown",
   "id": "a4777ed408714f44",
   "metadata": {},
   "source": [
    "### Evaluating a Policy\n",
    "\n",
    "The `evaluate_policy` function tests a policy by running it across the training dataset of an environment.\n",
    "\n",
    "#### Function Logic:\n",
    "1. **Reset the Environment**: Starts from the initial state.\n",
    "2. **Evaluate Episodes**: For each episode:\n",
    "   - The policy selects an action based on the current observation.\n",
    "   - The environment transitions to the next state, returning the reward, cost, and termination signal.\n",
    "   - Rewards and costs are accumulated.\n",
    "3. **Calculate Averages**: Computes the average reward and cost across all episodes.\n",
    "\n",
    "#### Outputs:\n",
    "- **`avg_reward`**: Average reward over episodes.\n",
    "- **`avg_cost`**: Average cost over episodes.\n"
   ]
  },
  {
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   "cell_type": "code",
   "source": [
    "def evaluate_policy(policy, env):\n",
    "    \"\"\"Evaluate the policy on the given environment.\"\"\"\n",
    "    env.idx = 0\n",
    "    total_reward = 0\n",
    "    total_cost = 0\n",
    "    for _ in range(len(env.train_data)):\n",
    "        obs = env.reset()\n",
    "        episode_reward = 0\n",
    "        episode_cost = 0\n",
    "        done = False\n",
    "        while not done:\n",
    "            action = policy.select_action(obs)\n",
    "            next_obs, reward, cost, done = env.step(action)\n",
    "            obs = next_obs\n",
    "            episode_reward += reward\n",
    "            episode_cost += cost\n",
    "        \n",
    "        #print(f\"index: {_}, episode reward: {episode_reward}, episode cost: {episode_cost}\")\n",
    "        total_reward += episode_reward\n",
    "        total_cost += episode_cost\n",
    "    \n",
    "    avg_reward = total_reward/len(env.train_data)\n",
    "    avg_cost = total_cost/len(env.train_data)\n",
    "\n",
    "    return avg_reward.item(), avg_cost.item()"
   ],
   "id": "3fd3215dc8d0e19c",
   "outputs": [],
   "execution_count": 25
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "### Training Loop Logic Summary\n",
    "\n",
    "The training loop is structured into **exploration**, **training**, and **evaluation** phases, allowing the agent to learn optimal policies while ensuring constraints are respected.\n",
    "\n",
    "---\n",
    "\n",
    "### Inputs\n",
    "\n",
    "- **`env`**: The training environment where the agent interacts and learns.\n",
    "- **`dev_env`**: A validation environment for policy evaluation.\n",
    "- **`agent`**: The reinforcement learning agent implementing the PD-DDPG algorithm.\n",
    "- **`replay_buffer`**: A memory buffer storing past experiences for training.\n",
    "- **`expl_episodes`**: Number of episodes dedicated to pure exploration before training begins.\n",
    "- **`expl_noise`**: Initial exploration noise, which decays over time.\n",
    "- **`batch_size`**: The number of samples drawn from the replay buffer for each training step.\n",
    "- **`num_epochs`**: The number of training iterations over the dataset.\n",
    "- **`iterations`**: Number of gradient updates per training step.\n",
    "- **`discount`**: The discount factor (`γ`), controlling future reward importance.\n",
    "- **`policy_freq`**: Frequency at which the actor network is updated.\n",
    "- **`tolerance`**: Stability threshold for early stopping.\n",
    "- **`eval_every`**: Frequency of policy evaluation during training.\n",
    "\n",
    "---\n",
    "\n",
    "### Key Steps\n",
    "\n",
    "1. **Initialization**:\n",
    "   - Initialize episode counters, evaluation logs, and timing.\n",
    "   - Print the training start time.\n",
    "   - Conduct an **initial policy evaluation** on `dev_env`.\n",
    "\n",
    "2. **Exploration Phase** (if enabled):\n",
    "   - The agent performs **random actions** to explore the environment.\n",
    "   - Experience tuples `(state, next_state, action, reward, cost, done)` are stored in the replay buffer.\n",
    "   - A progress bar tracks the completion of exploration episodes.\n",
    "\n",
    "3. **Training Phase**:\n",
    "   - Iterate over multiple **epochs**, processing all training data:\n",
    "     - Reset the environment and initialize observations.\n",
    "     - Loop through environment interactions:\n",
    "       - The agent selects an action using its policy, with added noise for exploration.\n",
    "       - The action is executed in the environment, and a transition is recorded.\n",
    "       - The replay buffer stores the experience for later training.\n",
    "       - Training occurs when enough samples are available.\n",
    "     - Exploration noise **decays gradually** to shift from exploration to exploitation.\n",
    "\n",
    "4. **Policy Evaluation**:\n",
    "   - Every `eval_every` episodes, the agent is evaluated on `dev_env`.\n",
    "   - Compute average reward, cost, and log the **dual variable (`λ`)**.\n",
    "   - Logs are updated, and early stopping is triggered if performance stabilizes. To check the stability of the agent's policy, we implemented the function `is_stabilized()`.\n",
    "\n",
    "5. **Logging & Early Stopping**:\n",
    "   - Track training progress, including rewards, costs, and policy parameters.\n",
    "   - If the agent's performance stabilizes (reward and cost remain within a tolerance window), **training stops early**.\n",
    "\n",
    "6. **Output**:\n",
    "   - Returns evaluation histories (`eval_reward_hist`, `eval_cost_hist`) for analysis.\n",
    "\n"
   ],
   "id": "aa42dcfae94b4106"
  },
  {
   "cell_type": "code",
   "id": "762b99d8587b86cb",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-31T01:28:17.340781Z",
     "start_time": "2025-01-31T01:28:17.332455Z"
    }
   },
   "source": [
    "from tqdm import tqdm\n",
    "from datetime import datetime\n",
    "import time\n",
    "\n",
    "def training_loop(env, dev_env, agent, replay_buffer, expl_episodes, expl_noise, batch_size, num_epochs, iterations, discount, policy_freq, tolerance, eval_every):\n",
    "    \"\"\"\n",
    "    Main training loop for the RL agent with early stopping and a single progress bar for the exploration phase.\n",
    "\n",
    "    Args:\n",
    "        env: Training environment.\n",
    "        dev_env: Evaluation (development) environment.\n",
    "        agent: The RL agent being trained.\n",
    "        replay_buffer: Buffer to store transitions for training.\n",
    "        expl_episodes: Number of episodes dedicated to random exploration.\n",
    "        expl_noise: Initial exploration noise for policy-based action selection.\n",
    "        batch_size: Number of samples used per training batch.\n",
    "        num_epochs: Total number of epochs for training.\n",
    "        iterations: Number of gradient update steps during training.\n",
    "        discount: Discount factor for future rewards.\n",
    "        policy_freq: Frequency of policy updates relative to critic updates.\n",
    "        tolerance: Stabilization tolerance for early stopping.\n",
    "        eval_every: Number of episodes between evaluations.\n",
    "\n",
    "    Returns:\n",
    "        eval_reward_hist: History of evaluation rewards.\n",
    "        eval_cost_hist: History of evaluation costs.\n",
    "    \"\"\"\n",
    "\n",
    "    # Initialize counters and evaluation history\n",
    "    episode, epoch, eval_time = 0, 0, 0\n",
    "    eval_reward_hist, eval_cost_hist = [], []\n",
    "    start_time = time.time()\n",
    "    print(f\"\\n========== Training Started at {datetime.now().strftime('%Y-%m-%d %H:%M:%S')} ==========\")\n",
    "    \n",
    "    # Evaluate the policy before training begins\n",
    "    eval_reward, eval_cost = evaluate_policy(agent, dev_env)\n",
    "    print(f\"\\n[EVALUATION] Episode {episode} | Reward: {eval_reward} | Cost: {eval_cost} | Lambda: {agent.lambda_.item():.4f} | Noise: {expl_noise:.2f}\")\n",
    "    \n",
    "    # Exploration Phase: Agent performs random actions to populate replay buffer\n",
    "    if expl_episodes > 0:\n",
    "        with tqdm(total=expl_episodes, desc=\"Exploration Phase\", leave=True) as expl_pbar:\n",
    "            while episode < expl_episodes:\n",
    "                done, obs = False, env.reset()  # Reset the environment at the start of each episode\n",
    "                while not done:\n",
    "                    # Take a random action from the action space\n",
    "                    action = env.action_space.sample().reshape(1, 1)\n",
    "                    # Step through the environment\n",
    "                    next_obs, reward, cost, done = env.step(action)\n",
    "                    # Store transition in the replay buffer\n",
    "                    agent.store_transition(replay_buffer, obs, next_obs, action.reshape(1,), reward, cost, done)\n",
    "                    obs = next_obs  # Update observation for the next step\n",
    "                episode += 1  # Increment episode count\n",
    "                expl_pbar.update(1)  # Update exploration progress bar\n",
    "\n",
    "    # Training Phase: Agent trains using stored transitions in the replay buffer\n",
    "    while epoch < num_epochs:\n",
    "        for idx in range(len(env.train_data)):\n",
    "\n",
    "            # Perform policy evaluation at regular intervals\n",
    "            if episode % eval_every == 0:\n",
    "                start_eval = time.time()\n",
    "                eval_reward, eval_cost = evaluate_policy(agent, dev_env)  # Evaluate on dev environment\n",
    "                eval_time += time.time() - start_eval\n",
    "                eval_reward_hist.append(eval_reward)\n",
    "                eval_cost_hist.append(eval_cost)\n",
    "                print(\"-------------------------------------------------\")\n",
    "                print(f\"\\n[EVALUATION on DEV DATA] at Episode {episode} | Avg. Reward: {eval_reward} | Avg. Cost: {eval_cost} | Lambda: {agent.lambda_.item():.4f} | Expl Noise: {expl_noise:.2f}\")\n",
    "                print(\"-------------------------------------------------\")\n",
    "\n",
    "                # Early stopping if metrics stabilize\n",
    "                if len(eval_cost_hist) > 5 and is_stabilized(eval_cost_hist, eval_reward_hist, tolerance=tolerance, window_size=5):\n",
    "                    print(\"****  Early stopping triggered. Training stabilized.  ****\")\n",
    "                    return _log_training_times(start_time, eval_time, eval_reward_hist, eval_cost_hist)\n",
    "\n",
    "            # Progress bar for training episodes\n",
    "            with tqdm(total=eval_every, desc=f\"Training --- Episodes {episode}-{episode+eval_every-1}\", leave=True) as pbar:\n",
    "                for _ in range(eval_every):\n",
    "                    done, obs = False, env.reset()  # Reset environment at start of each episode\n",
    "                    while not done:\n",
    "                        # Select action with exploration noise\n",
    "                        action = (\n",
    "                            agent.select_action(obs) + \n",
    "                            np.random.normal(0, expl_noise, size=env.action_space.shape[0])\n",
    "                        ).clip(env.action_space.low, env.action_space.high)\n",
    "                        # Step in the environment and collect transition\n",
    "                        next_obs, reward, cost, done = env.step(action)\n",
    "                        agent.store_transition(replay_buffer, obs, next_obs, action.reshape(1,), reward, cost, done)\n",
    "                        obs = next_obs  # Update observation\n",
    "\n",
    "                    # Train the agent when the replay buffer has enough data\n",
    "                    if len(replay_buffer.storage) > batch_size:\n",
    "                        agent.train(replay_buffer, iterations, batch_size, discount, policy_freq)\n",
    "\n",
    "                    # Decay exploration noise after each episode\n",
    "                    expl_noise = max(0.1, expl_noise * 0.995)\n",
    "                    episode += 1  # Increment episode count\n",
    "                    pbar.update(1)  # Update training progress bar\n",
    "\n",
    "        print(f\"Epoch {epoch} completed. Episodes: {episode}.\")\n",
    "        epoch += 1  # Increment epoch count\n",
    "\n",
    "    # Log training times and return evaluation history\n",
    "    return _log_training_times(start_time, eval_time, eval_reward_hist, eval_cost_hist)\n",
    "\n",
    "def _log_training_times(start_time, eval_time, eval_reward_hist, eval_cost_hist):\n",
    "    \"\"\"\n",
    "    Log total and pure training times.\n",
    "\n",
    "    Args:\n",
    "        start_time (float): Start time of the training.\n",
    "        eval_time (float): Total time spent on evaluation.\n",
    "        eval_reward_hist (list): History of evaluation rewards.\n",
    "        eval_cost_hist (list): History of evaluation costs.\n",
    "\n",
    "    Returns:\n",
    "        tuple: History of rewards and costs.\n",
    "    \"\"\"\n",
    "    total_time = time.time() - start_time\n",
    "    print(\"\\n========== Training Completed ==========\\n\")\n",
    "    print(f\"Total Time: {total_time:.2f}s | Pure Training Time: {total_time - eval_time:.2f}s | Eval Time: {eval_time:.2f}s\")\n",
    "    return eval_reward_hist, eval_cost_hist\n"
   ],
   "outputs": [],
   "execution_count": 26
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "### Stabilization Check for Early Stopping\n",
    "\n",
    "The `is_stabilized` function determines if training has **converged** by monitoring reward and cost fluctuations over recent iterations. This prevents unnecessary training.\n",
    "\n",
    "---\n",
    "\n",
    "### Inputs\n",
    "- **`eval_rewards`**, **`eval_costs`**: Lists or tensors of historical evaluations.\n",
    "- **`tolerance`**: Maximum allowed change for stability (default: `10`).\n",
    "- **`window_size`**: Number of recent evaluations to compare (default: `5`).\n",
    "\n",
    "---\n",
    "\n",
    "### Key Steps\n",
    "1. **Convert to NumPy** (if input is a tensor).\n",
    "2. **Check Data Availability** (requires at least `2 * window_size` samples).\n",
    "3. **Compute Moving Averages** for rewards and costs over the last `window_size` evaluations.\n",
    "4. **Assess Stability** by comparing recent and previous averages:\n",
    "   - If both **reward and cost fluctuations** are within `tolerance`, training is considered stable.\n",
    "\n",
    "---\n",
    "\n",
    "### Output\n",
    "- **`True`** → Training has stabilized, early stopping is recommended.\n",
    "- **`False`** → Further training is needed."
   ],
   "id": "10a2ba7752227611"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-31T01:28:17.379487Z",
     "start_time": "2025-01-31T01:28:17.375327Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import numpy as np\n",
    "\n",
    "def is_stabilized(eval_rewards, eval_costs, tolerance=10, window_size=5):\n",
    "    \"\"\"\n",
    "    Check if evaluation metrics (rewards and costs) have stabilized.\n",
    "\n",
    "    Args:\n",
    "        eval_rewards (list or tensor): History of reward evaluations.\n",
    "        eval_costs (list or tensor): History of cost evaluations.\n",
    "        tolerance (float): Maximum allowed difference for stabilization.\n",
    "        window_size (int): Number of recent evaluations to consider.\n",
    "\n",
    "    Returns:\n",
    "        bool: True if stabilized, False otherwise.\n",
    "    \"\"\"\n",
    "    # Convert inputs to NumPy arrays if they're tensors\n",
    "    eval_rewards = np.array(eval_rewards) if isinstance(eval_rewards, (list, np.ndarray)) else eval_rewards.cpu().numpy()\n",
    "    eval_costs = np.array(eval_costs) if isinstance(eval_costs, (list, np.ndarray)) else eval_costs.cpu().numpy()\n",
    "    \n",
    "    # Ensure there are enough evaluations for comparison\n",
    "    if len(eval_rewards) < 2 * window_size or len(eval_costs) < 2 * window_size:\n",
    "        return False\n",
    "\n",
    "    # Calculate recent and previous averages for rewards and costs\n",
    "    recent_avg_rewards = np.mean(eval_rewards[-window_size:])\n",
    "    previous_avg_rewards = np.mean(eval_rewards[-2 * window_size:-window_size])\n",
    "    recent_avg_costs = np.mean(eval_costs[-window_size:])\n",
    "    previous_avg_costs = np.mean(eval_costs[-2 * window_size:-window_size])\n",
    "\n",
    "    # Check if differences are within the tolerance\n",
    "    reward_stable = abs(recent_avg_rewards - previous_avg_rewards) <= tolerance\n",
    "    cost_stable = abs(recent_avg_costs - previous_avg_costs) <= tolerance\n",
    "\n",
    "    return reward_stable and cost_stable"
   ],
   "id": "ad09e0844e87899c",
   "outputs": [],
   "execution_count": 27
  },
  {
   "cell_type": "markdown",
   "id": "6ea083afe77edd1f",
   "metadata": {},
   "source": [
    "### Training the RL agent\n",
    "\n",
    "We have already generated the required data and initiated the environments for training the agent. Now, we initiate the agent, replay buffer, and set the parameters of training "
   ]
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-31T01:28:17.426102Z",
     "start_time": "2025-01-31T01:28:17.423704Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Initialize agent parameters\n",
    "\n",
    "lambda_ = 1.  # Initial dual variable\n",
    "lambda_step = 3e-5  # Step size for updating lambda\n",
    "constraint_limit = 0.01 * 50.  # Constraint tolerance limit\n",
    "lr_critic = 1e-3  # Learning rate for the critic network\n",
    "lr_actor = 1e-4  # Learning rate for the actor network\n",
    "hidden_layers = [32, 32]  # Hidden layer dimensions for neural networks\n",
    "iterations = 200  # Number of training iterations\n",
    "policy_freq = 2  # Frequency of policy updates\n",
    "discount = 0.95  # Discount factor for rewards\n",
    "\n",
    "\n",
    "# Define state and action dimensions\n",
    "state_dim = train_env.observation_space.shape[0]  # Environment state dimension\n",
    "action_dim = train_env.action_space.shape[0]  # Environment action dimension\n",
    "max_action = 1.0  # Maximum action value\n",
    "\n",
    "\n",
    "# Initialize agent\n",
    "agent = PD_DDPG(state_dim, action_dim, max_action, lambda_, lambda_step, constraint_limit, lr_critic, lr_actor, hidden_layers)\n",
    "\n",
    "\n",
    "#initialize replay buffer\n",
    "replay_buffer = ReplayBuffer(1e6)\n",
    "\n",
    "# Initialize training loop parameters\n",
    "num_epochs = 5         #Number of epochs\n",
    "expl_episodes = 200     #Number of exploration episodes\n",
    "batch_size = 128        #Batch size for training the agent\n",
    "expl_noise = 0.3        #initial exploration noise\n",
    "tolerance=5             #Thereshold for policy convergence\n",
    "eval_every=10           #Frequency of episodes to do evaluation on policy "
   ],
   "id": "9767e807c76a942b",
   "outputs": [],
   "execution_count": 28
  },
  {
   "cell_type": "code",
   "id": "432cf73b5dd9efa",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-31T01:36:17.333268Z",
     "start_time": "2025-01-31T01:28:17.508922Z"
    }
   },
   "source": [
    "#starting training process:\n",
    "eval_reward_hist, eval_cost_hist = training_loop(train_env, dev_env, agent,\n",
    "                                                 replay_buffer, expl_episodes,\n",
    "                                                 expl_noise, batch_size, num_epochs,\n",
    "                                                 iterations, discount, policy_freq,\n",
    "                                                 tolerance,eval_every)"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "========== Training Started at 2025-01-30 20:28:17 ==========\n",
      "\n",
      "[EVALUATION] Episode 0 | Reward: -2706.52880859375 | Cost: 3600.296875 | Lambda: 1.0000 | Noise: 0.30\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Exploration Phase: 100%|██████████| 200/200 [00:05<00:00, 36.77it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------------\n",
      "\n",
      "[EVALUATION on DEV DATA] at Episode 200 | Avg. Reward: -2706.52880859375 | Avg. Cost: 3600.296875 | Lambda: 1.0000 | Expl Noise: 0.30\n",
      "-------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training --- Episodes 200-209: 100%|██████████| 10/10 [00:17<00:00,  1.75s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------------\n",
      "\n",
      "[EVALUATION on DEV DATA] at Episode 210 | Avg. Reward: -396.79132080078125 | Avg. Cost: 3556.28759765625 | Lambda: 1.7851 | Expl Noise: 0.29\n",
      "-------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training --- Episodes 210-219: 100%|██████████| 10/10 [00:13<00:00,  1.40s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------------\n",
      "\n",
      "[EVALUATION on DEV DATA] at Episode 220 | Avg. Reward: -1447.731689453125 | Avg. Cost: 401.5229797363281 | Lambda: 2.2494 | Expl Noise: 0.27\n",
      "-------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training --- Episodes 220-229: 100%|██████████| 10/10 [00:13<00:00,  1.32s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------------\n",
      "\n",
      "[EVALUATION on DEV DATA] at Episode 230 | Avg. Reward: -1285.04736328125 | Avg. Cost: 393.5538330078125 | Lambda: 2.5816 | Expl Noise: 0.26\n",
      "-------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training --- Episodes 230-239: 100%|██████████| 10/10 [00:12<00:00,  1.26s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------------\n",
      "\n",
      "[EVALUATION on DEV DATA] at Episode 240 | Avg. Reward: -1209.942626953125 | Avg. Cost: 408.2552490234375 | Lambda: 2.9034 | Expl Noise: 0.25\n",
      "-------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training --- Episodes 240-249: 100%|██████████| 10/10 [00:12<00:00,  1.25s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------------\n",
      "\n",
      "[EVALUATION on DEV DATA] at Episode 250 | Avg. Reward: -1168.3216552734375 | Avg. Cost: 403.5782165527344 | Lambda: 3.2196 | Expl Noise: 0.23\n",
      "-------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training --- Episodes 250-259: 100%|██████████| 10/10 [00:12<00:00,  1.26s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------------\n",
      "\n",
      "[EVALUATION on DEV DATA] at Episode 260 | Avg. Reward: -1156.0693359375 | Avg. Cost: 400.22515869140625 | Lambda: 3.5276 | Expl Noise: 0.22\n",
      "-------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training --- Episodes 260-269: 100%|██████████| 10/10 [00:12<00:00,  1.26s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------------\n",
      "\n",
      "[EVALUATION on DEV DATA] at Episode 270 | Avg. Reward: -1141.9693603515625 | Avg. Cost: 402.09295654296875 | Lambda: 3.8229 | Expl Noise: 0.21\n",
      "-------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training --- Episodes 270-279: 100%|██████████| 10/10 [00:12<00:00,  1.27s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------------\n",
      "\n",
      "[EVALUATION on DEV DATA] at Episode 280 | Avg. Reward: -1140.86767578125 | Avg. Cost: 393.66680908203125 | Lambda: 4.1162 | Expl Noise: 0.20\n",
      "-------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training --- Episodes 280-289: 100%|██████████| 10/10 [00:12<00:00,  1.29s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------------\n",
      "\n",
      "[EVALUATION on DEV DATA] at Episode 290 | Avg. Reward: -1139.8388671875 | Avg. Cost: 391.1136474609375 | Lambda: 4.4109 | Expl Noise: 0.19\n",
      "-------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training --- Episodes 290-299: 100%|██████████| 10/10 [00:13<00:00,  1.30s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------------\n",
      "\n",
      "[EVALUATION on DEV DATA] at Episode 300 | Avg. Reward: -1136.7742919921875 | Avg. Cost: 387.16656494140625 | Lambda: 4.6929 | Expl Noise: 0.18\n",
      "-------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training --- Episodes 300-309: 100%|██████████| 10/10 [00:12<00:00,  1.26s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------------\n",
      "\n",
      "[EVALUATION on DEV DATA] at Episode 310 | Avg. Reward: -1132.570556640625 | Avg. Cost: 386.5024719238281 | Lambda: 4.9737 | Expl Noise: 0.17\n",
      "-------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training --- Episodes 310-319: 100%|██████████| 10/10 [00:12<00:00,  1.26s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------------\n",
      "\n",
      "[EVALUATION on DEV DATA] at Episode 320 | Avg. Reward: -1128.427490234375 | Avg. Cost: 385.42120361328125 | Lambda: 5.2474 | Expl Noise: 0.16\n",
      "-------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training --- Episodes 320-329: 100%|██████████| 10/10 [00:12<00:00,  1.28s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------------\n",
      "\n",
      "[EVALUATION on DEV DATA] at Episode 330 | Avg. Reward: -1127.70263671875 | Avg. Cost: 382.494384765625 | Lambda: 5.5165 | Expl Noise: 0.16\n",
      "-------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training --- Episodes 330-339: 100%|██████████| 10/10 [00:13<00:00,  1.33s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------------\n",
      "\n",
      "[EVALUATION on DEV DATA] at Episode 340 | Avg. Reward: -1131.1719970703125 | Avg. Cost: 378.79522705078125 | Lambda: 5.7798 | Expl Noise: 0.15\n",
      "-------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training --- Episodes 340-349: 100%|██████████| 10/10 [00:13<00:00,  1.32s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------------\n",
      "\n",
      "[EVALUATION on DEV DATA] at Episode 350 | Avg. Reward: -1123.603271484375 | Avg. Cost: 381.58160400390625 | Lambda: 6.0355 | Expl Noise: 0.14\n",
      "-------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training --- Episodes 350-359: 100%|██████████| 10/10 [00:13<00:00,  1.33s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------------\n",
      "\n",
      "[EVALUATION on DEV DATA] at Episode 360 | Avg. Reward: -1123.029541015625 | Avg. Cost: 378.9532470703125 | Lambda: 6.2890 | Expl Noise: 0.13\n",
      "-------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training --- Episodes 360-369: 100%|██████████| 10/10 [00:12<00:00,  1.29s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------------\n",
      "\n",
      "[EVALUATION on DEV DATA] at Episode 370 | Avg. Reward: -1124.0179443359375 | Avg. Cost: 377.7292175292969 | Lambda: 6.5439 | Expl Noise: 0.13\n",
      "-------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training --- Episodes 370-379: 100%|██████████| 10/10 [00:12<00:00,  1.27s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------------\n",
      "\n",
      "[EVALUATION on DEV DATA] at Episode 380 | Avg. Reward: -1117.56591796875 | Avg. Cost: 378.2841491699219 | Lambda: 6.7861 | Expl Noise: 0.12\n",
      "-------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training --- Episodes 380-389: 100%|██████████| 10/10 [00:13<00:00,  1.30s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------------\n",
      "\n",
      "[EVALUATION on DEV DATA] at Episode 390 | Avg. Reward: -1117.6632080078125 | Avg. Cost: 377.4623718261719 | Lambda: 7.0219 | Expl Noise: 0.12\n",
      "-------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training --- Episodes 390-399: 100%|██████████| 10/10 [00:12<00:00,  1.28s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------------\n",
      "\n",
      "[EVALUATION on DEV DATA] at Episode 400 | Avg. Reward: -1111.07470703125 | Avg. Cost: 378.4710998535156 | Lambda: 7.2552 | Expl Noise: 0.11\n",
      "-------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training --- Episodes 400-409: 100%|██████████| 10/10 [00:12<00:00,  1.27s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------------\n",
      "\n",
      "[EVALUATION on DEV DATA] at Episode 410 | Avg. Reward: -1108.8187255859375 | Avg. Cost: 378.2857360839844 | Lambda: 7.4870 | Expl Noise: 0.10\n",
      "-------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training --- Episodes 410-419: 100%|██████████| 10/10 [00:12<00:00,  1.27s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------------\n",
      "\n",
      "[EVALUATION on DEV DATA] at Episode 420 | Avg. Reward: -1110.00048828125 | Avg. Cost: 377.05133056640625 | Lambda: 7.7257 | Expl Noise: 0.10\n",
      "-------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training --- Episodes 420-429: 100%|██████████| 10/10 [00:13<00:00,  1.34s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------------\n",
      "\n",
      "[EVALUATION on DEV DATA] at Episode 430 | Avg. Reward: -1108.604736328125 | Avg. Cost: 377.00457763671875 | Lambda: 7.9617 | Expl Noise: 0.10\n",
      "-------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training --- Episodes 430-439: 100%|██████████| 10/10 [00:13<00:00,  1.33s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------------\n",
      "\n",
      "[EVALUATION on DEV DATA] at Episode 440 | Avg. Reward: -1105.4638671875 | Avg. Cost: 375.75360107421875 | Lambda: 8.2045 | Expl Noise: 0.10\n",
      "-------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training --- Episodes 440-449: 100%|██████████| 10/10 [00:13<00:00,  1.32s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------------\n",
      "\n",
      "[EVALUATION on DEV DATA] at Episode 450 | Avg. Reward: -1104.7021484375 | Avg. Cost: 374.97027587890625 | Lambda: 8.4395 | Expl Noise: 0.10\n",
      "-------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training --- Episodes 450-459: 100%|██████████| 10/10 [00:13<00:00,  1.31s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------------\n",
      "\n",
      "[EVALUATION on DEV DATA] at Episode 460 | Avg. Reward: -1103.6968994140625 | Avg. Cost: 374.2486572265625 | Lambda: 8.6738 | Expl Noise: 0.10\n",
      "-------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training --- Episodes 460-469: 100%|██████████| 10/10 [00:13<00:00,  1.32s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------------\n",
      "\n",
      "[EVALUATION on DEV DATA] at Episode 470 | Avg. Reward: -1101.91650390625 | Avg. Cost: 374.7168884277344 | Lambda: 8.9046 | Expl Noise: 0.10\n",
      "-------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training --- Episodes 470-479: 100%|██████████| 10/10 [00:13<00:00,  1.32s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------------\n",
      "\n",
      "[EVALUATION on DEV DATA] at Episode 480 | Avg. Reward: -1108.0838623046875 | Avg. Cost: 371.8150329589844 | Lambda: 9.1381 | Expl Noise: 0.10\n",
      "-------------------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Training --- Episodes 480-489: 100%|██████████| 10/10 [00:13<00:00,  1.35s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------------\n",
      "\n",
      "[EVALUATION on DEV DATA] at Episode 490 | Avg. Reward: -1102.2738037109375 | Avg. Cost: 372.18878173828125 | Lambda: 9.3641 | Expl Noise: 0.10\n",
      "-------------------------------------------------\n",
      "****  Early stopping triggered. Training stabilized.  ****\n",
      "\n",
      "========== Training Completed ==========\n",
      "\n",
      "Total Time: 479.82s | Pure Training Time: 391.45s | Eval Time: 88.38s\n"
     ]
    }
   ],
   "execution_count": 29
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-31T01:52:02.272822Z",
     "start_time": "2025-01-31T01:52:02.061485Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# Plot evaluation rewards and costs\n",
    "x = range(1, len(eval_reward_hist) + 1)\n",
    "plt.figure(figsize=(14, 6))\n",
    "plt.plot(x, eval_reward_hist, label=\"Total Rewards\", color='blue', marker='o', linewidth=2)\n",
    "plt.plot(x, eval_cost_hist, label=\"Total Costs\", color='red', marker='o', linewidth=2)\n",
    "plt.xlabel(\"Evaluation Number\", fontsize=14)\n",
    "plt.ylabel(\"Value\", fontsize=14)\n",
    "plt.title(\"RL Agent Performance Over Evaluations\", fontsize=16)\n",
    "plt.grid(True, linestyle='--', linewidth=0.5)\n",
    "plt.legend(fontsize=12)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ],
   "id": "14543b9ba4f20645",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1400x600 with 1 Axes>"
      ],
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 47
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "## Differentiable Predictive Control\n",
    "\n",
    "Now in this section, we implement and train the DPC agent using the same dataset generated for the RL environment. This ensures both methods are trained and evaluated on identical conditions, allowing for a fair comparison. The implementation of DPC for different problems are already available in Nueromancer examples, such as [DPC Building Control](https://github.com/pnnl/neuromancer/blob/master/examples/domain_examples/DPC_building_control.ipynb) and [DPC for Two Connected Tanks](https://github.com/pnnl/neuromancer/blob/master/examples/control/Part_3_ref_tracking_ODE.ipynb).\n",
    "\n",
    "---\n",
    "\n",
    "### State-Space Model\n",
    "\n",
    "We use the same state-space model from the RL environment to define the system dynamics.\n",
    "\n",
    "---\n",
    "\n",
    "### Control Policy\n",
    "\n",
    "A neural control policy **$u_k = \\pi(x_k, R)$** computes the control action **$u_k$** based on the system state **$x_k$** and reference temperature bounds **$R = [T_{\\text{min}}, T_{\\text{max}}]$**. The policy is trained using gradient-based optimization.\n",
    "\n",
    "---\n",
    "\n",
    "### Optimization Problem Formulation\n",
    "\n",
    "The control policy is trained by solving the following **constrained optimization problem**:\n",
    "\n",
    "$$\n",
    "\\begin{align}\n",
    "&\\underset{\\theta}{\\text{minimize}} && \\sum_{k=1}^{N-1} \\Big( 0.01 ||u_k||_2^2 + 0.1 ||u_k - u_{k-1}||_2^2 \\Big) \\\\\n",
    "&\\text{subject to} && x_{k+1} = A x_k + B u_k + E d_k \\\\\n",
    "& && u_k = \\pi_{\\theta}(x_k, d_k, R_k) \\\\\n",
    "& && y_k = C x_k \\\\\n",
    "& && T_{\\text{min}, k} \\leq y_k \\leq T_{\\text{max}, k} \\\\\n",
    "& && 0 \\leq u_k \\leq 1 \\\\\n",
    "& && x_0 \\sim \\mathcal{P}_{x_0} \\\\\n",
    "& && d_k \\sim \\mathcal{P}_d \\\\\n",
    "& && R \\sim \\mathcal{P}_R\n",
    "\\end{align}\n",
    "$$\n",
    "\n",
    "where:\n",
    "- **Objective Function**: Minimizes energy usage **$||u_k||_2^2$** and penalizes abrupt control action changes **$||u_k - u_{k-1}||_2^2$**.\n",
    "- **Constraints**: Ensure temperature remains within the reference bounds **$[T_{\\text{min}, k}, T_{\\text{max}, k}]$** and control actions stay within operational limits **$[0, 1]$**.\n",
    "- **Stochasticity**: Distributions **$\\mathcal{P}_{x_0}$**, **$\\mathcal{P}_d$**, and **$\\mathcal{P}_R$** account for initial states, disturbances, and reference variations.\n",
    "\n",
    "---\n",
    "\n",
    "### Constraints\n",
    "\n",
    "To enforce constraints, penalties are applied:\n",
    "\n",
    "$$\n",
    "\\text{Constraint Violations: } \\quad \n",
    "\\begin{align}\n",
    "50 \\cdot \\max(T_{\\text{min}, k} - y_k, 0), \\\\\n",
    "50 \\cdot \\max(y_k - T_{\\text{max}, k}, 0).\n",
    "\\end{align}\n",
    "$$\n",
    "\n",
    "These penalties ensure the policy prioritizes keeping indoor temperature within acceptable limits.\n",
    "\n",
    "---\n",
    "\n",
    "### Training and Implementation\n",
    "\n",
    "The optimization problem is solved using **stochastic gradient descent (SGD)** over sampled initial conditions, disturbances, and reference bounds. The training process includes:\n",
    "\n",
    "- **Data normalization** for system states, disturbances, and reference signals.\n",
    "- **Batch-wise optimization** using the AdamW optimizer.\n",
    "- **Dynamic penalty updates** to ensure constraint satisfaction.\n",
    "\n",
    "The same dataset used for training the RL agent is applied here, ensuring both learning paradigms operate under identical conditions. The learned policy is evaluated on unseen scenarios to verify its generalization capabilities.\n"
   ],
   "id": "a2d6281c4e6528a0"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "### System model and Control policy in Neuromancer\n",
    "\n",
    "Here we construct a closed-loop system as differentiable computational graph by connecting the system dynamics model  $x_{k+1} = \\text{ODESolve}(f(x_k, u_k))$ with neural control policy $u_k = \\pi_{\\theta}(x_k, R)$. Hence we obtain a trainable system architecture:\n",
    "$x_{k+1} = \\text{ODESolve}(f(x_k, \\pi_{\\theta}(x_k, R)))$."
   ],
   "id": "928923d5adbcdbf8"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-31T01:36:17.474544Z",
     "start_time": "2025-01-31T01:36:17.356946Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import neuromancer.psl as psl\n",
    "from neuromancer.system import Node, System\n",
    "from neuromancer.modules import blocks\n",
    "from neuromancer.modules.activations import activations\n",
    "from neuromancer.dataset import DictDataset\n",
    "from neuromancer.constraint import variable\n",
    "from neuromancer.loss import PenaltyLoss\n",
    "from neuromancer.problem import Problem\n",
    "from neuromancer.trainer import Trainer\n",
    "\n",
    "\n",
    "# state-space model of the building dynamics:\n",
    "#   x_k+1 =  A x_k + B u_k + E d_k\n",
    "xnext = lambda x, u, d: x @ problem_specs[\"A\"].T + u @ problem_specs[\"B\"].T + d @ problem_specs[\"E\"].T\n",
    "state_model = Node(xnext, ['x', 'u', 'd'], ['x'], name='SSM')\n",
    "#   y_k = C x_k\n",
    "ynext = lambda x: x @ problem_specs[\"C\"].T\n",
    "output_model = Node(ynext, ['x'], ['y'], name='y=Cx')\n",
    "\n",
    "# get normalization layer to generate policy features\n",
    "def normalize_features(*inputs):\n",
    "    x = torch.cat(inputs, dim=-1)\n",
    "    return (x - data_stats[\"means\"]) / data_stats[\"stds\"]\n",
    "\n",
    "# features node\n",
    "features = Node(normalize_features, ['x', 'ymin', 'ymax', 'd'],\n",
    "                ['xi'], name='features')\n",
    "\n",
    "# neural net control policy\n",
    "net = blocks.MLP_bounds(insize=problem_specs[\"nx\"] + 2 * problem_specs[\"nref\"] + problem_specs[\"nd\"],\n",
    "                        outsize=problem_specs[\"nu\"], hsizes=[32, 32],\n",
    "                        nonlin=activations['gelu'],\n",
    "                        min=problem_specs[\"umin\"], max=problem_specs[\"umax\"])\n",
    "# symbolic policy\n",
    "policy = Node(net, ['xi'], ['u'], name='policy')\n",
    "\n",
    "# closed-loop system model\n",
    "cl_system = System([features, policy, state_model, output_model],\n",
    "                   nsteps=nsteps,\n",
    "                   name='cl_system')\n",
    "cl_system.show()\n"
   ],
   "id": "c2aeb654e45f6073",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 30
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "### Differentiable Predictive Control objectives and constraints\n",
    "\n",
    "Here we take advantage of Neuromancer's high level symbolic language to define objective and constraint terms of our optimal control problem.\n",
    "\n"
   ],
   "id": "3e6c47ae03dc85ea"
  },
  {
   "cell_type": "code",
   "id": "515a0c17bec12a64",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-31T01:36:17.492565Z",
     "start_time": "2025-01-31T01:36:17.486514Z"
    }
   },
   "source": [
    "# variables\n",
    "y = variable('y')\n",
    "u = variable('u')\n",
    "ymin = variable('ymin')\n",
    "ymax = variable('ymax')\n",
    "\n",
    "# objectives\n",
    "action_loss = 0.01 * (u == 0.0)  # energy minimization\n",
    "du_loss = 0.1 * (u[:,:-1,:] - u[:,1:,:] == 0.0)  # delta u minimization\n",
    "\n",
    "# # constraints\n",
    "state_lower_bound_penalty = 50.*(y > ymin)\n",
    "state_upper_bound_penalty = 50.*(y < ymax)\n",
    "\n",
    "# # objectives and constraints names for nicer plot\n",
    "action_loss.name = 'action_loss'\n",
    "du_loss.name = 'du_loss'\n",
    "state_lower_bound_penalty.name = 'x_min'\n",
    "state_upper_bound_penalty.name = 'x_max'\n",
    "\n",
    "# list of constraints and objectives\n",
    "objectives = [action_loss, du_loss]\n",
    "constraints = [\n",
    "    state_lower_bound_penalty,\n",
    "    state_upper_bound_penalty\n",
    "]\n"
   ],
   "outputs": [],
   "execution_count": 31
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "### Differentiable optimal control problem\n",
    "\n",
    "Here we put things together to construct a differentibale optimal control problem.\n"
   ],
   "id": "25a41b3a1d0d5485"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-31T01:36:17.611981Z",
     "start_time": "2025-01-31T01:36:17.536153Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# data -> parameters (xi_k) -> policy (u_k) -> dynamics (x_k+1)\n",
    "nodes = [cl_system]\n",
    "\n",
    "# create constrained optimization loss\n",
    "loss = PenaltyLoss(objectives, constraints)\n",
    "\n",
    "# construct constrained optimization problem\n",
    "problem = Problem(nodes, loss)\n",
    "\n",
    "# plot computational graph\n",
    "problem.show()\n"
   ],
   "id": "3821e486586eeabc",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 32
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "### Solving the problem\n",
    "\n",
    "We solve the problem using stochastic gradient descent over pre-defined training data of sampled parameters.\n"
   ],
   "id": "86cca7ba5d9ca434"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-31T01:42:23.346500Z",
     "start_time": "2025-01-31T01:36:17.633321Z"
    }
   },
   "cell_type": "code",
   "source": [
    "epochs = 300\n",
    "optimizer = torch.optim.AdamW(problem.parameters(), lr=0.001)\n",
    "#  Neuromancer trainer\n",
    "trainer = Trainer(\n",
    "    problem,\n",
    "    data_loader[\"train_loader\"], data_loader[\"dev_loader\"],\n",
    "    optimizer=optimizer,\n",
    "    epochs=epochs,\n",
    "    train_metric='train_loss',\n",
    "    eval_metric='dev_loss',\n",
    "    warmup=epochs,\n",
    "    patience=5,\n",
    ")\n",
    "start_time = time.time()\n",
    "# Train control policy\n",
    "best_model = trainer.train()\n",
    "# load best trained model\n",
    "trainer.model.load_state_dict(best_model)\n",
    "\n",
    "print(f\"Pure Training Time: {time.time() - start_time:.2f}s\")"
   ],
   "id": "a28f6c91e88eda9b",
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/alireza/neuromancer/neuromancer/neuromancer_RL/src/neuromancer/constraint.py:169: UserWarning: Using a target size (torch.Size([])) that is different to the input size (torch.Size([100, 100, 1])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n",
      "  loss = F.l1_loss(left, right)\n",
      "/home/alireza/neuromancer/neuromancer/neuromancer_RL/src/neuromancer/constraint.py:169: UserWarning: Using a target size (torch.Size([])) that is different to the input size (torch.Size([100, 99, 1])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n",
      "  loss = F.l1_loss(left, right)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch: 0  train_loss: 59.3846321105957\n",
      "epoch: 1  train_loss: 55.6896858215332\n",
      "epoch: 2  train_loss: 51.7288818359375\n",
      "epoch: 3  train_loss: 47.29197311401367\n",
      "epoch: 4  train_loss: 42.26031494140625\n",
      "epoch: 5  train_loss: 36.91016387939453\n",
      "epoch: 6  train_loss: 31.81292724609375\n",
      "epoch: 7  train_loss: 27.44415283203125\n",
      "epoch: 8  train_loss: 24.189727783203125\n",
      "epoch: 9  train_loss: 21.964689254760742\n",
      "epoch: 10  train_loss: 20.414411544799805\n",
      "epoch: 11  train_loss: 19.338672637939453\n",
      "epoch: 12  train_loss: 18.57314682006836\n",
      "epoch: 13  train_loss: 18.00468635559082\n",
      "epoch: 14  train_loss: 17.58952522277832\n",
      "epoch: 15  train_loss: 17.284473419189453\n",
      "epoch: 16  train_loss: 17.052677154541016\n",
      "epoch: 17  train_loss: 16.864383697509766\n",
      "epoch: 18  train_loss: 16.70281410217285\n",
      "epoch: 19  train_loss: 16.56045150756836\n",
      "epoch: 20  train_loss: 16.434192657470703\n",
      "epoch: 21  train_loss: 16.32189178466797\n",
      "epoch: 22  train_loss: 16.221267700195312\n",
      "epoch: 23  train_loss: 16.129291534423828\n",
      "epoch: 24  train_loss: 16.0457706451416\n",
      "epoch: 25  train_loss: 15.97096061706543\n",
      "epoch: 26  train_loss: 15.905588150024414\n",
      "epoch: 27  train_loss: 15.849459648132324\n",
      "epoch: 28  train_loss: 15.79969596862793\n",
      "epoch: 29  train_loss: 15.7555570602417\n",
      "epoch: 30  train_loss: 15.716659545898438\n",
      "epoch: 31  train_loss: 15.681447982788086\n",
      "epoch: 32  train_loss: 15.649693489074707\n",
      "epoch: 33  train_loss: 15.6221284866333\n",
      "epoch: 34  train_loss: 15.598462104797363\n",
      "epoch: 35  train_loss: 15.577105522155762\n",
      "epoch: 36  train_loss: 15.559446334838867\n",
      "epoch: 37  train_loss: 15.543696403503418\n",
      "epoch: 38  train_loss: 15.529245376586914\n",
      "epoch: 39  train_loss: 15.515838623046875\n",
      "epoch: 40  train_loss: 15.503698348999023\n",
      "epoch: 41  train_loss: 15.491604804992676\n",
      "epoch: 42  train_loss: 15.480313301086426\n",
      "epoch: 43  train_loss: 15.468849182128906\n",
      "epoch: 44  train_loss: 15.458253860473633\n",
      "epoch: 45  train_loss: 15.446664810180664\n",
      "epoch: 46  train_loss: 15.43567180633545\n",
      "epoch: 47  train_loss: 15.424201965332031\n",
      "epoch: 48  train_loss: 15.413250923156738\n",
      "epoch: 49  train_loss: 15.401537895202637\n",
      "epoch: 50  train_loss: 15.389925003051758\n",
      "epoch: 51  train_loss: 15.378216743469238\n",
      "epoch: 52  train_loss: 15.365966796875\n",
      "epoch: 53  train_loss: 15.35383415222168\n",
      "epoch: 54  train_loss: 15.340906143188477\n",
      "epoch: 55  train_loss: 15.328404426574707\n",
      "epoch: 56  train_loss: 15.31495475769043\n",
      "epoch: 57  train_loss: 15.302044868469238\n",
      "epoch: 58  train_loss: 15.287782669067383\n",
      "epoch: 59  train_loss: 15.274354934692383\n",
      "epoch: 60  train_loss: 15.259706497192383\n",
      "epoch: 61  train_loss: 15.245048522949219\n",
      "epoch: 62  train_loss: 15.230720520019531\n",
      "epoch: 63  train_loss: 15.215917587280273\n",
      "epoch: 64  train_loss: 15.200677871704102\n",
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      "epoch: 69  train_loss: 15.121015548706055\n",
      "epoch: 70  train_loss: 15.104100227355957\n",
      "epoch: 71  train_loss: 15.08833122253418\n",
      "epoch: 72  train_loss: 15.07208251953125\n",
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      "epoch: 75  train_loss: 15.027827262878418\n",
      "epoch: 76  train_loss: 15.013259887695312\n",
      "epoch: 77  train_loss: 14.998631477355957\n",
      "epoch: 78  train_loss: 14.983739852905273\n",
      "epoch: 79  train_loss: 14.970852851867676\n",
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      "epoch: 81  train_loss: 14.946650505065918\n",
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      "epoch: 288  train_loss: 14.659013748168945\n",
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      "epoch: 294  train_loss: 14.660268783569336\n",
      "epoch: 295  train_loss: 14.664484024047852\n",
      "epoch: 296  train_loss: 14.663409233093262\n",
      "epoch: 297  train_loss: 14.660577774047852\n",
      "epoch: 298  train_loss: 14.656791687011719\n",
      "epoch: 299  train_loss: 14.654376029968262\n",
      "Pure Training Time: 365.71s\n"
     ]
    }
   ],
   "execution_count": 33
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "### Compute DPC Costs `(calculate_dpc_costs)`\n",
    "\n",
    "This function calculates the **operational cost** and **constraint violation cost** for a given trajectory.\n",
    "\n",
    "- **Operational Cost**: Summed over all control actions and scaled to reflect energy usage.\n",
    "- **Violation Cost**: Penalizes deviations from temperature constraints by applying a weighted penalty when the temperature exceeds the upper or lower bounds.\n"
   ],
   "id": "375df88fccebd7b6"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-31T01:42:23.360274Z",
     "start_time": "2025-01-31T01:42:23.357143Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def calculate_dpc_costs(trajectories):\n",
    "    \"\"\"Compute operational and violation costs from a trajectory.\"\"\"\n",
    "    temp_min, temp_max = trajectories[\"ymin\"][0].clone().detach(), trajectories[\"ymax\"][0].clone().detach()\n",
    "    dpc_operational_cost = sum(u.item() for u in trajectories['u'][0]) * 0.01\n",
    "\n",
    "    dpc_violation_cost = sum(\n",
    "        50 * max(temp_min[i].item() - y.item(), 0) +\n",
    "        50 * max(y.item() - temp_max[i].item(), 0)\n",
    "        for i, y in enumerate(trajectories['y'][0][:-1])\n",
    "    )\n",
    "\n",
    "    return dpc_operational_cost, dpc_violation_cost\n"
   ],
   "id": "61fa73cf4f9b689",
   "outputs": [],
   "execution_count": 34
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "### Evaluating DPC over Dev Data\n",
    "\n",
    "This function evaluates the trained DPC on a given dataset by computing the **average operational cost** and **average constraint violation cost** across multiple samples.\n",
    "\n",
    "#### Process:\n",
    "1. **Iterates through all samples** in the dataset.\n",
    "2. **Prepares input data** for each sample, including initial state, disturbance, and reference bounds.\n",
    "3. **Runs closed-loop simulations** for each sample.\n",
    "4. **Computes costs** for each trajectory using the **operational cost** (energy usage) and **violation cost** (penalties for exceeding constraints).\n",
    "5. **Averages costs** over all samples to assess the overall efficiency and constraint adherence of the control policy.\n",
    "\n",
    "#### Output:\n",
    "- **Average Operational Cost**\n",
    "- **Average Violation Cost**"
   ],
   "id": "ea059cdd74ee484a"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-31T01:42:23.418517Z",
     "start_time": "2025-01-31T01:42:23.413541Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def evaluate_dpc(dpc, data):\n",
    "    total_operational_cost = 0.0\n",
    "    total_violation_cost = 0.0\n",
    "    num_samples = len(data)\n",
    "\n",
    "    for idx, sample in enumerate(data):\n",
    "        # Prepare sample data\n",
    "        x0 = sample['x'].unsqueeze(dim=0)\n",
    "        ymin = sample['ymin'].unsqueeze(dim=0)\n",
    "        ymax = sample['ymax'].unsqueeze(dim=0)\n",
    "        d = sample['d'].unsqueeze(dim=0)\n",
    "        nsteps = len(sample['ymin'])\n",
    "\n",
    "        sample_data = {\n",
    "            'x': x0,\n",
    "            'y': x0[:, :, [-1]],\n",
    "            'ymin': ymin,\n",
    "            'ymax': ymax,\n",
    "            'd': d\n",
    "        }\n",
    "\n",
    "        # Set the simulation steps\n",
    "        dpc.nsteps = nsteps\n",
    "\n",
    "        # Perform closed-loop simulation\n",
    "        trajectories = dpc(sample_data)\n",
    "\n",
    "        # Compute costs for the current trajectory\n",
    "        operational_cost, violation_cost = calculate_dpc_costs(trajectories)\n",
    "        \n",
    "        # Accumulate total costs\n",
    "        total_operational_cost += operational_cost\n",
    "        total_violation_cost += violation_cost\n",
    "\n",
    "    # Compute average costs\n",
    "    avg_operational_cost = total_operational_cost / num_samples\n",
    "    avg_violation_cost = total_violation_cost / num_samples\n",
    "\n",
    "    return avg_operational_cost, avg_violation_cost\n"
   ],
   "id": "33734fd523c39257",
   "outputs": [],
   "execution_count": 35
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-31T01:42:30.487237Z",
     "start_time": "2025-01-31T01:42:23.463990Z"
    }
   },
   "cell_type": "code",
   "source": [
    "dpc_operational_cost, dpc_violation_cost = evaluate_dpc(cl_system, data['dev_data'])\n",
    "rl_operational_cost, rl_violation_cost = evaluate_policy(agent, dev_env)\n",
    "\n",
    "print(f\"DPC Average Operational Cost on Dev Data: {dpc_operational_cost:.2f}\")\n",
    "print(f\"RL Average Operational Cost on Dev Data: {-rl_operational_cost:.2f}\")\n",
    "print(f\"DPC Average Violation Cost on Dev Data: {dpc_violation_cost:.2f}\")\n",
    "print(f\"RL Average Violation Cost on Dev Data: {rl_violation_cost:.2f}\")"
   ],
   "id": "a6c3fd84277ce1fe",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DPC Average Operational Cost on Dev Data: 1069.68\n",
      "RL Average Operational Cost on Dev Data: 1102.27\n",
      "DPC Average Violation Cost on Dev Data: 514.61\n",
      "RL Average Violation Cost on Dev Data: 372.19\n"
     ]
    }
   ],
   "execution_count": 36
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "### DPC vs RL Rollout Simulation\n",
    "\n"
   ],
   "id": "38335b743d83d7fc"
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "#### A function for visualization:",
   "id": "b81d680dc400ace9"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-31T02:40:35.601898Z",
     "start_time": "2025-01-31T02:40:35.589449Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def visualize_results(rl_outputs, dpc_trajectories, inf_data):\n",
    "    \n",
    "    # Temperature Control Plot\n",
    "    plt.figure(figsize=(20, 5))\n",
    "    plt.plot([temp[0][0].item() for temp in rl_outputs['y']], label=\"Controlled Temp (RL)\", color='blue', linestyle='-', linewidth=4, alpha=0.7)\n",
    "    plt.plot([temp[0].cpu().detach() for temp in dpc_trajectories['y'][0]], label=\"Controlled Temp (DPC)\", color='red', linestyle='-', linewidth=4, alpha=0.7)\n",
    "\n",
    "    time_steps = np.arange(len(inf_data['ymin'][0]) - 1)\n",
    "    plt.step(time_steps, np.ravel(inf_data['ymin'][0][:-1]), where='post', label=\"Min Temp Bound\", color='green', linestyle='--', linewidth=2)\n",
    "    plt.step(time_steps, np.ravel(inf_data['ymax'][0][:-1]), where='post', label=\"Max Temp Bound\", color='orange', linestyle='--', linewidth=2)\n",
    "    plt.fill_between(time_steps, np.ravel(inf_data['ymin'][0][:-1]), np.ravel(inf_data['ymax'][0][:-1]),\n",
    "                     color='yellow', alpha=0.3, label='Valid Temp Range')\n",
    "\n",
    "    plt.xlabel(\"Time Step\", fontsize=14)\n",
    "    plt.ylabel(\"Temperature (°C)\", fontsize=14)\n",
    "    plt.title(\"Controlled Zone Temperature (RL vs. DPC)\", fontsize=18)\n",
    "    plt.grid(True, linestyle='--', linewidth=0.5)\n",
    "    plt.legend(fontsize=12, loc='best')\n",
    "    plt.tight_layout()\n",
    "\n",
    "\n",
    "    # Plot RL and DPC actions\n",
    "    plt.figure(figsize=(20, 5))\n",
    "    plt.plot([(action[0][0].item()) for action in rl_outputs['u']], label=\"Actions (RL)\", color='blue', linestyle='-', linewidth=4, alpha=0.7)\n",
    "    plt.plot([action.cpu().detach() for action in dpc_trajectories['u'][0]], label=\"Actions (DPC)\", color='red', linestyle='-', linewidth=4, alpha=0.7)\n",
    "\n",
    "    plt.ylim(0, problem_specs['umax'].numpy())\n",
    "    plt.xlabel(\"Time Step\", fontsize=14)\n",
    "    plt.ylabel(\"Actions (Scaled)\", fontsize=14)\n",
    "    plt.title(\"Actions Taken (RL vs. DPC)\", fontsize=18)\n",
    "    plt.grid(True, linestyle='--', linewidth=0.5)\n",
    "    plt.legend(fontsize=12, loc='upper right')\n",
    "    plt.tight_layout()\n",
    "    \n",
    "    \n",
    "    # Compute action differences\n",
    "    rl_action_diff = [abs(rl_outputs['u'][i+1][0][0].item() - rl_outputs['u'][i][0][0].item()) for i in range(len(rl_outputs['u']) - 1)]\n",
    "    dpc_action_diff = [abs(dpc_trajectories['u'][0][i+1].cpu().detach() - dpc_trajectories['u'][0][i].cpu().detach()) for i in range(len(dpc_trajectories['u'][0]) - 1)]\n",
    "\n",
    "    # Plot difference in consecutive actions\n",
    "    plt.figure(figsize=(20, 5))\n",
    "    plt.plot(rl_action_diff, label=\"Action Difference (RL)\", color='blue', linestyle='-', linewidth=4, alpha=0.5)\n",
    "    plt.plot(dpc_action_diff, label=\"Action Difference (DPC)\", color='red', linestyle='-', linewidth=4, alpha=0.7)\n",
    "\n",
    "    plt.xlabel(\"Time Step\", fontsize=14)\n",
    "    plt.ylabel(\"Change in Action\", fontsize=14)\n",
    "    plt.title(\"Difference Between Consecutive Actions (RL vs. DPC)\", fontsize=18)\n",
    "    plt.grid(True, linestyle='--', linewidth=0.5)\n",
    "    plt.legend(fontsize=12, loc='upper right')\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "    # Plot disturbances\n",
    "    plt.figure(figsize=(20, 5))\n",
    "    plt.plot(inf_data[\"d\"][0][:,0].numpy(), label=\"Outdoor Air Temp\", color='red', linestyle='-', linewidth=4)\n",
    "    plt.plot(inf_data[\"d\"][0][:,1].numpy(), label=\"Occupant Heat Load\", color='green', linestyle='-', linewidth=4)\n",
    "    plt.plot(inf_data[\"d\"][0][:,2].numpy(), label=\"Solar Radiation\", color='blue', linestyle='-', linewidth=4)\n",
    "\n",
    "    plt.xlabel(\"Time Step\", fontsize=14)\n",
    "    plt.ylabel(\"Disturbances\", fontsize=14)\n",
    "    plt.title(\"External Disturbances Over Time\", fontsize=18)\n",
    "    plt.grid(True, linestyle='--', linewidth=0.5)\n",
    "    plt.legend(fontsize=12, loc='upper right')\n",
    "    plt.tight_layout()\n",
    "\n",
    "    plt.show()\n",
    "\n",
    "    #Calculate operational cost and violations\n",
    "    rl_operational_cost = -1 * sum(rl_outputs[\"rewards\"]).item()\n",
    "    rl_violation_cost = sum(rl_outputs[\"costs\"]).item()\n",
    "\n",
    "    dpc_operational_cost, dpc_violation_cost = calculate_dpc_costs(dpc_trajectories)\n",
    "    # Bar plot for costs with dual axes\n",
    "    labels = ['Operational Cost', 'Violation Cost']\n",
    "    rl_costs = [rl_operational_cost, rl_violation_cost]\n",
    "    dpc_costs = [dpc_operational_cost, dpc_violation_cost]\n",
    "\n",
    "    x = np.arange(len(labels))\n",
    "    width = 0.2\n",
    "\n",
    "    fig, ax1 = plt.subplots(figsize=(15, 6))\n",
    "\n",
    "    # Plot operational costs on the left y-axis\n",
    "    bars_rl = ax1.bar(x - width/2, rl_costs, width, label='RL', color='blue', alpha=0.7)\n",
    "    bars_dpc = ax1.bar(x + width/2, dpc_costs, width, label='DPC', color='red', alpha=0.7)\n",
    "    ax1.set_ylabel('Operational Cost', fontsize=14)\n",
    "    ax1.set_ylim(0, max(rl_operational_cost, dpc_operational_cost) * 1.2)\n",
    "    ax1.set_xticks(x)\n",
    "    ax1.set_xticklabels(labels, fontsize=12)\n",
    "    ax1.set_title('Cost Comparison: RL vs. DPC', fontsize=16)\n",
    "\n",
    "    # Add a secondary y-axis for violation costs\n",
    "    ax2 = ax1.twinx()\n",
    "    ax2.set_ylabel('Violation Cost', fontsize=14)\n",
    "    ax2.set_ylim(0, max(rl_violation_cost, dpc_violation_cost) * 1.2)\n",
    "\n",
    "    # Add the violation costs as bars but differentiate them visually\n",
    "    bars_rl_violation = ax2.bar(x - width/2, [0, rl_violation_cost], width, label='RL Violation', color='darkblue', alpha=0.5, hatch='//')\n",
    "    bars_dpc_violation = ax2.bar(x + width/2, [0, dpc_violation_cost], width, label='DPC Violation', color='darkred', alpha=0.5, hatch='//')\n",
    "\n",
    "    # Combine legends\n",
    "    lines1, labels1 = ax1.get_legend_handles_labels()\n",
    "    lines2, labels2 = ax2.get_legend_handles_labels()\n",
    "    ax1.legend(lines1 + lines2, labels1 + labels2, loc='upper left', fontsize=12)\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "    print(\"=== Cost Analysis ===\")\n",
    "    print(f\"RL Operational Cost: {rl_operational_cost:.2f}\")\n",
    "    print(f\"DPC Operational Cost: {dpc_operational_cost:.2f}\")\n",
    "    print(f\"RL Constraint Violation Cost: {rl_violation_cost:.2f}\")\n",
    "    print(f\"DPC Constraint Violation Cost: {dpc_violation_cost:.2f}\")"
   ],
   "id": "98d20065b4734ccb",
   "outputs": [],
   "execution_count": 88
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "#### Rollout Simulation for Both RL and DPC",
   "id": "a8946f405f2f6c22"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-31T02:41:15.178971Z",
     "start_time": "2025-01-31T02:41:13.878695Z"
    }
   },
   "cell_type": "code",
   "source": [
    "nsteps_test = 1000\n",
    "sys = psl.systems['LinearSimpleSingleZone']()\n",
    "# generate reference\n",
    "np_refs = psl.signals.step(nsteps_test+1, 1, min=18., max=22., randsteps=5)\n",
    "ymin_val = torch.tensor(np_refs, dtype=torch.float32).reshape(1, nsteps_test+1, 1)\n",
    "ymax_val = ymin_val+2.0\n",
    "# generate disturbance signal\n",
    "torch_dist = torch.tensor(sys.get_D(nsteps_test+1)).unsqueeze(0)\n",
    "# initial data for closed loop simulation\n",
    "x0 = torch.tensor(sys.get_x0()).reshape(1, 1, problem_specs[\"nx\"])\n",
    "inf_data = {'x': x0,\n",
    "        'y': x0[:, :, [-1]],\n",
    "        'ymin': ymin_val,\n",
    "        'ymax': ymax_val,\n",
    "        'd': torch_dist}\n",
    "\n",
    "train_env.reset()\n",
    "outputs = train_env.inference(inf_data, agent)\n",
    "\n",
    "cl_system.nsteps = nsteps_test\n",
    "# perform closed-loop simulation\n",
    "trajectories = cl_system(inf_data)\n",
    "\n"
   ],
   "id": "400952557e313665",
   "outputs": [],
   "execution_count": 99
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-01-31T02:41:16.119093Z",
     "start_time": "2025-01-31T02:41:15.276513Z"
    }
   },
   "cell_type": "code",
   "source": "visualize_results(outputs, trajectories, inf_data)",
   "id": "32c7d0acb7340f24",
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/alireza/anaconda3/envs/neuromancer/lib/python3.10/site-packages/numpy/core/shape_base.py:65: FutureWarning: The input object of type 'Tensor' is an array-like implementing one of the corresponding protocols (`__array__`, `__array_interface__` or `__array_struct__`); but not a sequence (or 0-D). In the future, this object will be coerced as if it was first converted using `np.array(obj)`. To retain the old behaviour, you have to either modify the type 'Tensor', or assign to an empty array created with `np.empty(correct_shape, dtype=object)`.\n",
      "  ary = asanyarray(ary)\n",
      "/home/alireza/anaconda3/envs/neuromancer/lib/python3.10/site-packages/numpy/core/shape_base.py:65: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
      "  ary = asanyarray(ary)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 2000x500 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 2000x500 with 1 Axes>"
      ],
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 2000x500 with 1 Axes>"
      ],
      "image/png": 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sExMTo2+//VaLFy9Wq1atNHHiRH3wwQfq3bt3hZ8zCvPy8nJ3CABQpZFnAcBavCMMAKxHnxYArEWeBQBr2Ww2d4dw2al0P9kMwyhxfXR0tH766aeL7qd+/fr67rvvSmzTvXt3bdy4sUzxoWLs379fUVFR7g4DAKos8iwAWCs5OVmBgYHuDgMAqjT6tABgLfIsAFgrOztb3t7e7g7jssIwBgAAAAAAAAAAAABAlUZhHJVSixYt3B0CAFRp5FkAsBYjawDAevRpAcBa5Fmg6hk0aJAaNGjg7jBKZfny5bLZbFq+fLnT8k8++URNmzaVt7e3QkNDzeUTJkxQw4YN5enpqdatW1dorOXl7+9v2b7tdrvi4+P1yiuvWHaM8srNzVV0dLSmTp1a4cemMI5K6eDBg+4OAQCqNPIsAFgrJSXF3SEAQJVHnxYArEWeBSre1KlTZbPZ1KlTp3Lv4+jRoxo7dqw2bdrkusAu0f79+2Wz2cwvb29v1ahRQ1dddZWeffbZUuebnTt3atCgQYqNjdX777+v9957T5K0aNEiPf300+rSpYtmzJihV1991crTcZmcnBzL9j179mwdOnRII0aMMJfNnDnT6XPw8/NT7dq11bt3b/3rX/9SWlpaof2MHTvWaZuAgABdccUVev7555Wamlqo/d69ezV06FA1bNhQfn5+Cg4OVpcuXfTWW28pMzNTkuTt7a0nnnhCr7zyirKysiy7BkWpdO8YByT+kAgAViPPAoC1KvoXOwC4HNGnBQBrkWeBipeQkKAGDRrot99+0549exQXF1fmfRw9elTjxo1TgwYNCo2cfv/992W3210UbdndeeeduuGGG2S323X27FmtXbtWU6ZM0VtvvaXp06dr4MCBZtuuXbsqMzNTPj4+5rLly5fLbrfrrbfecro2S5culYeHh6ZPn+7UvrLLz8+3bN8TJkzQwIEDFRISUmjd+PHjFRMTo9zcXCUlJWn58uUaOXKkJk2apAULFqhly5aFtnn33XcVGBio9PR0LVq0SK+88oqWLl2qX375RTabTZL07bff6o477pCvr6/uu+8+xcfHKycnRytXrtSoUaO0bds282GGBx54QM8884w+/fRTPfjgg5ZdhwtRGEel5Ofn5+4QAKBKI88CgLW8vAr/qmUY0n9/VwQAuAB9WgCwFnkWqFiJiYlatWqV5s6dq6FDhyohIUFjxoxx6TG8vb1dur+yatu2re655x6nZQcOHFCvXr10//33q1mzZmrVqpUkycPDo1AeOnHihCQ5TaHuWO7v7+/Sovi5c+cUEBDgsv0VxcPDmom9N27cqM2bN2vixIlFru/Tp4/at29v/nv06NFaunSpbrrpJt1yyy3asWNHoWneb7/9dtWoUUOSNGzYMPXr109z587Vr7/+qs6dOysxMVEDBw5U/fr1tXTpUtWqVcvcdvjw4dqzZ4++/fZbc1loaKh69eqlmTNnVmhhnKnUUSnFx8e7OwQAqNLIswBgrcjIyELLDMMNgQBAFUafFgCsRZ4FKlZCQoLCwsJ044036vbbb1dCQkKR7ZKTk/X444+rQYMG8vX1Vd26dXXffffp1KlTWr58uTp06CDp/Ihcx/TXM2fOlFT0O8YzMjL05JNPKjo6Wr6+vmrSpInefPNNGRf8Emuz2TRixAjNnz9f8fHx8vX1VfPmzfXDDz9c0nnXr19fM2fOVE5Ojt544w1z+YXvGG/QoIH5oEBERIRsNps5zfeMGTOUkZFR6HwladasWWrXrp38/f0VHh6ugQMH6tChQ04xdO/eXfHx8Vq/fr26du2qgIAAPfvss5Kk7OxsjRkzRnFxcfL19VV0dLSefvppZWdnl/v6HDlyRIMHDzb3GRMTo0ceecRpavXk5GSNHDnS/Fzi4uL0z3/+s1Qj/ufPny8fHx917dr1om0drr32Wr3wwgs6cOCAZs2aVar20vkHOiTpjTfeUHp6uqZPn+5UFHeIi4vTY4895rTs+uuv18qVK3XmzJlSx3mpGDGOSmndunWX9A4NAEDJyLMAYK2jR4+qbt26TssojAOAa9GnBQBrkWdRqb39tnTggLujKFr9+tLf/17mzRISEnTbbbfJx8dHd955p959912tXbvWLHRLUnp6uq655hrt2LFDDz74oNq2batTp05pwYIFOnz4sJo1a6bx48frxRdf1JAhQ3TNNddIkq666qoij2kYhm655RYtW7ZMgwcPVuvWrbVw4UKNGjVKR44c0eTJk53ar1y5UnPnztXf/vY3BQUF6V//+pf69eungwcPqnr16mU+Z4fOnTsrNjZWixcvLrbNlClT9PHHH2vevHnmtN4tW7ZUXFyc3nvvPf3222/64IMPnM73lVde0QsvvKD+/fvroYce0smTJ/X222+ra9eu2rhxo9PI89OnT6tPnz4aOHCg7rnnHkVFRclut+uWW27RypUrNWTIEDVr1ky///67Jk+erN27d2v+/Pllvj5Hjx5Vx44dlZycrAceeEAtWrTQkSNH9OWXX+rcuXPy8fHRuXPn1K1bNx05ckRDhw5VvXr1tGrVKo0ePVrHjh3TlClTSryeq1atUnx8fJlnCLj33nv17LPPatGiRXr44YdLbLt3715JMs/rP//5jxo2bFjsvVaUdu3ayTAMrVq1SjfddFOZYi0vCuMAAAAAUAEojAMAAACAixw4IO3a5e4oXGb9+vXauXOn3n77bUnS1Vdfrbp16yohIcGpMD5hwgRt3bpVc+fO1V//+ldz+fPPPy/DMGSz2dSnTx+9+OKL6ty5c6Fpyy+0YMECLV26VC+//LKee+45Seenvb7jjjv01ltvacSIEYqNjTXb79ixQ9u3bzeX9ejRQ61atdLs2bM1YsSIS7oG8fHx+vrrr5Wamqrg4OBC6/v27atNmzZp3rx5TtN6t2zZUkuWLNGGDRuczvfAgQMaM2aMXn75ZXP0tyTddtttatOmjaZOneq0PCkpSdOmTdPQoUPNZbNmzdKSJUv0008/6eqrr3aKddiwYVq1apVTIbg012f06NFKSkrSmjVr1LRpUwUGBko6/95vxyj9SZMmae/evdq4caMaNWokSRo6dKhq166tCRMmmCP8i7Nz585yPdhUt25dhYSEmEXvghyjuh3vGJ86daqioqJ0zTXXKDU1VUeOHNGtt95apuM1bNhQkrR9+/YKK4wzlToqpaKmWQAAuA55FgCsFRQU5O4QAKDKo08LANYizwIVJyEhQVFRUerRo4ek89NyDxgwQHPmzFF+fr7Z7quvvlKrVq2ciuIONputzMf97rvv5OnpqUcffdRp+ZNPPinDMPT99987Lb/uuuucCuUtW7ZUcHCw9u3bV+ZjX8hRIE5LS7vkfUnS3LlzZbfb1b9/f506dcr8qlmzpho1aqRly5Y5tff19dUDDzzgtOyLL75Qs2bN1LRpU6d9OKYRv3AfF7s+drtd8+fP180336z27dsXGtHt+Ay/+OILXXPNNQoLC3M67nXXXaf8/HytWLGixHM/ffq0wsLCynC1/icwMLDIz6BJkyaKiIhQTEyMhg4dqri4OH377bcKCAhQamqqpLL/LcQR46lTp8oVa3kwYhyVUkBAgLtDAIAqjTwLANYqaroyRowDgGvRpwUAa5FngYqRn5+vOXPmqEePHub7miWpU6dOmjhxon788Uf16tVL0vnpq/v16+eyYx84cEC1a9cuVNBs1qyZub6gevXqFdpHWFiYzp49e8mxpKenS3Ldg+Z//PGHDMMwR1xf6MLf2+vUqSMfH59C+9ixY4ciIiKK3MeJEyec/n2x63Py5EmlpqYqPj5ekuThUfT45T/++ENbtmwp9XGLcuE74ksrPT1dkZGRhZZ/9dVXCg4Olre3t+rWrev0AIBjhH9ZH2pwxFiehzrKi8I4KqW9e/ea02AAAFyPPAsA1jpz5kyhPyRSGAcA16JPCwDWIs8CFWPp0qU6duyY5syZozlz5hRan5CQYBbG3c3T07PI5eUtwha0detWRUZGFjmNennY7XbZbDZ9//33RcbtGKHu4O/vX+Q+WrRooUmTJhV5jAunMy/r9cnOzi7ywXq73a7rr79eTz/9dJHbNW7cuMjlDtWrVy/XwwqHDx9WSkqK4uLiCq3r2rVrsT8TgoODVbt2bW3durVMx3PEWJE/ayiMAwAAAEAFoDAOAAAAAC5Sv767IyheGWNLSEhQZGSk3nnnnULr5s6dq3nz5mnatGny9/dXbGzsRYuPZRl9W79+fS1ZskRpaWlOI7V37txprq8Iq1ev1t69ey/6TvSyiI2NlWEYiomJuWghuaR9bN68WT179nTJqOaIiAgFBwdf9DOMjY1Venq6rrvuunIdp2nTpk6zD5TWJ598Iknq3bt3mbe96aab9N5772n16tXq3LlzqbZxxOiYoaAiUBhHpdS8eXN3hwAAVRp5FgCsVdS0YxTGAcC16NMCgLXIs6jU/v53d0fgEpmZmZo7d67uuOMO3X777YXW165dW7Nnz9aCBQs0YMAA9evXT+PHj9e8efMKvWfcMAzZbDZVq1ZNkpScnHzR499www1677339O9//1ujR482l0+ePFk2m019+vS5tBMshQMHDmjQoEHy8fHRqFGjXLbf2267TaNHj9a4ceM0a9Ysp8K2YRg6c+aMqlevXuI++vfvr++++07vv/++hgwZ4rQuMzNTdrvdvN6l4eHhob59+2rWrFlat26d2rRp47Te8Rn2799fY8eO1cKFCwsVqZOTkxUYGCgvr+JLvJ07d9brr7+u7Oxs+fr6liq2pUuX6qWXXlJMTIzuvvvuUp+Tw9NPP62EhAQ99NBDWrp0qaKiopzW7927V998840ee+wxc9n69etls9lKXUh3BQrjqJSOHj1a7id4AAAXR54FAGulpaUV+gWbwjgAuBZ9WgCwFnkWsN6CBQuUlpamW265pcj1V155pSIiIpSQkKABAwZo1KhR+vLLL3XHHXfowQcfVLt27XTmzBktWLBA06ZNU6tWrRQbG6vQ0FBNmzZNQUFBqlatmjp16qSYmJhC+7/55pvVo0cPPffcc9q/f79atWqlRYsW6euvv9bIkSOd3iPtChs2bNCsWbNkt9uVnJystWvX6quvvpLNZtMnn3yili1buuxYsbGxevnllzV69Gjt379fffv2VVBQkBITEzVv3jwNGTJETz31VIn7uPfee/X5559r2LBhWrZsmbp06aL8/Hzt3LlTn3/+uRYuXKj27duXKa5XX31VixYtUrdu3fTggw+qRYsWOnbsmL744gutXLlSoaGhGjVqlBYsWKCbbrpJgwYNUrt27ZSRkaHff/9dX375pfbv31/i9OO33nqrXnrpJf30009FTsP//fffa+fOncrLy9Px48e1dOlSLV68WPXr19eCBQvk5+dXpnOSzl/vTz/9VAMGDFCzZs103333KT4+Xjk5OVq1apW++OILDRo0yGmbxYsXq0uXLhd9QMGVKIyjUirPuw8AAKVHngUAa2VmZhZaRmEcAFyLPi0AWIs8C1gvISFBfn5+uv7664tc7+HhoRtvvFEJCQk6ffq0qlevrp9//lljxozRvHnz9NFHHykyMlI9e/ZU3bp1JUne3t766KOPNHr0aA0bNkx5eXmaMWNGkYVxDw8PLViwQC+++KI+++wzzZgxQw0aNNCECRP05JNPuvx8Z8+erdmzZ8vLy0vBwcFq1KiRRo4cqWHDhqlevXouP94zzzyjxo0ba/LkyRo3bpyk8+8F79WrV7EPIxTk4eGh+fPna/Lkyfr44481b948BQQEqGHDhnrsscfK9fBQnTp1tGbNGr3wwguaM2eO3n//fdWpU0d9+vRRQECAJCkgIEA//fSTXn31VX3xxRf6+OOPFRwcrMaNG2vcuHEKCQkp8Rjt2rVTy5Yt9fnnnxdZGH/xxRclST4+PgoPD1eLFi00ZcoUPfDAA05T6pfVLbfcoi1btmjChAn6+uuv9e6778rX11ctW7bUxIkT9fDDD5ttU1JStGjRIk2dOrXcxysPm1HcG99RotTUVIWEhCglJUXBwcHuDqfK2bBhg9q2bevuMACgyiLPAoB1xo6Vjh07plq1ajktHzVKKsMMawCAi6BPCwDWIs+iMsnKylJiYqJiYmLKNZoVqIwyMjLKNBV7WXzyyScaPny4Dh48qNDQUEuOcSmmTJmiN954Q3v37pW/v3+JbS/2/V+Wmq3HJUUNWIQOFwBYizwLANa6sCguMWIcAFyNPi0AWIs8CwDWsqooLkl333236tWrp3feeceyY5RXbm6uJk2apOeff/6iRXFXozCOSmnNmjXuDgEAqjTyLABY6/Dhw4WWURgHANeiTwsA1iLPAoC10tPTLdu3h4eHtm7dqueee86yY5SXt7e3Dh48qL/97W8VfmwK4wAAAABQASiMAwAAAAAAuA+FcVRKUVFR7g4BAKo08iwAWCswMNDdIQBAlUefFgCsRZ4FAGt5e3u7O4TLDoVxVErBwcHuDgEAqjTyLABYy9fXt9AyRowDgGvRpwUAa5FnAcBanp6e7g7hskNhHJXSH3/84e4QAKBKI88CgLVOnz5daBmFcQBwLfq0AGAt8iwAWCsrK8vdIVx2KIwDAAAAQAWgMA4AAAAAl8bgFyvgsuPK73sK46iUmjVr5u4QAKBKI88CgLUiIiIKLePvNwDgWvRpAcBa5FlUJo53MZ87d87NkQCu4+/v7+4Q/hQyMjJks9lc8k52LxfEA7jciRMneIcNAFiIPAsA1srIyCj0nnEK4wDgWvRpAcBa5FlUJp6engoNDdWJEyckSQEBAbLZbG6OCrg02dnZhf52gPMMw1BeXp5SU1OVmpqq0NBQl7yTncI4KqXTp08rLi7O3WEAQJVFngUAa507d07h4eFOyyiMA4Br0acFAGuRZ1HZ1KxZU5LM4jjwZ0dh/OI8PT1Vq1YthYSEuGR/FMZRKbniqQ8AQPHIswBgLQ+Pwm+tojAOAK5FnxYArEWeRWVjs9lUq1YtRUZGKjc3193hAJds69atatq0qbvDqLS8vLzk6enp0tkhbIYr31h+GUlNTVVISIhSUlKYTgYAAACAaezYopf/7W9SZGSFhgIAAAAAAFCllaVmW3gYA1AJrF271t0hAECVRp4FAGsdOXKk0DIeSQYA16JPCwDWIs8CgLXIsxWPwjgqJbvd7u4QAKBKI88CgLWYmAsArEefFgCsRZ4FAGuRZysehXFUShEREe4OAQCqNPIsAFjDUQ+vVq1asesAAK5BnxYArEWeBQBrkWcrHoVxVErh4eHuDgEAqjTyLABYy9/fv9AyCuMA4Fr0aQHAWuRZALAWebbiURhHpbRr1y53hwAAVRp5FgCs4Sh+nzp1qth1AADXoE8LANYizwKAtcizFY/COAAAAABUAArjAAAAAAAA7kNhHJVS48aN3R0CAFRp5FkAsIaj+F29evVi1wEAXIM+LQBYizwLANYiz1Y8CuOolM6ePevuEACgSiPPAoA1HMXvrKysYtcBAFyDPi0AWIs8CwDWIs9WPArjqJROnjzp7hAAoEojzwKAtTIyMgotozAOAK5FnxYArEWeBQBrkWcrHoVxVEo2m83dIQBAlUaeBQBrOIrfReVZCuMA4Fr0aQHAWuRZALAWebbi2QyDP8+UR2pqqkJCQpSSkqLg4GB3hwMAAACgEsjJkV59teh1gwZJDRpUZDQAAAAAAABVW1lqtowYR6W0fv16d4cAAFUaeRYArHX06NFCy3gkGQBciz4tAFiLPAsA1iLPVjwK46iU8vLy3B0CAFRp5FkAsIaj+G23290bCABcBujTAoC1yLMAYC3ybMWjMI5KKTw83N0hAECVRp4FAGv5+/sXWsaIcQBwLfq0AGAt8iwAWIs8W/EojKNSioqKcncIAFClkWcBwBqO4ndgYGCx6wAArkGfFgCsRZ4FAGuRZysehXFUSjt27HB3CABQpZFnAcAajuL3yZMni10HAHAN+rQAYC3yLABYizxb8SiMAwAAAEAFoDAOAAAAAADgPhTGUSnFxcW5OwQAqNLIswBgDUfxu6j3hFEYBwDXok8LANYizwKAtcizFY/COCql9PR0d4cAAFUaeRYArOEofufk5BS7DgDgGvRpAcBa5FkAsBZ5tuJRGEellJSU5O4QAKBKI88CgLWK+uWWwjgAuBZ9WgCwFnkWAKxFnq14FMYBAAAAwEVKKn5TGAcAAAAAAHAfCuOolDp27OjuEACgSiPPAoC16tSpU2gZhXEAcC36tABgLfIsAFiLPFvxKIyjUtq0aZO7QwCAKo08CwDWcBS/i5oOjcI4ALgWfVoAsBZ5FgCsRZ6teBTGUSnl5OS4OwQAqNLIswBgDUfxOz8/372BAMBlgD4tAFiLPAsA1iLPVjwK46iUQkND3R0CAFRp5FkAsJafn1+hZYwYBwDXok8LANYizwKAtcizFY/COCqlunXrujsEAKjSyLMAYA1H8Ts4OLjYdQAA16BPCwDWIs8CgLXIsxWPwjgqpa1bt7o7BACo0sizAGCtEydOFFpGYRwAXIs+LQBYizwLANYiz1Y8CuMAAAAA4CIlFb8pjAMAAAAAALgPhXFUSg0bNnR3CABQpZFnAcAajuJ3WFhYsesAAK5BnxYArEWeBQBrkWcrXqUrjL/22mvq0KGDgoKCFBkZqb59+2rXrl1ObbKysjR8+HBVr15dgYGB6tevn44fP+7U5uDBg7rxxhsVEBCgyMhIjRo1Snl5eU5tli9frrZt28rX11dxcXGaOXOm1aeHUsrKynJ3CABQpZFnAcBaF/7uIVEYBwBXo08LANYizwKAtcizFa/SFcZ/+uknDR8+XL/++qsWL16s3Nxc9erVSxkZGWabxx9/XP/5z3/0xRdf6KefftLRo0d12223mevz8/N14403KicnR6tWrdJHH32kmTNn6sUXXzTbJCYm6sYbb1SPHj20adMmjRw5Ug899JAWLlxYoeeLoh09etTdIQBAlUaeBQBrOIrfaWlpxa4DALgGfVoAsBZ5FgCsRZ6teF7uDuBCP/zwg9O/Z86cqcjISK1fv15du3ZVSkqKpk+frk8//VTXXnutJGnGjBlq1qyZfv31V1155ZVatGiRtm/friVLligqKkqtW7fWSy+9pH/84x8aO3asfHx8NG3aNMXExGjixImSpGbNmmnlypWaPHmyevfuXeHnDQAAAODPj3eMAwAAAAAAVE6VbsT4hVJSUiRJ4eHhkqT169crNzdX1113ndmmadOmqlevnlavXi1JWr16tVq0aKGoqCizTe/evZWamqpt27aZbQruw9HGsQ+4V7t27dwdAgBUaeRZALBW7dq1Cy2jMA4ArkWfFgCsRZ4FAGuRZytepS6M2+12jRw5Ul26dFF8fLwkKSkpST4+PgoNDXVqGxUVpaSkJLNNwaK4Y71jXUltUlNTlZmZWSiW7OxspaamOn3BOo4HGAAA1iDPAoA1HMXvEydOFLsOAOAa9GkBwFrkWQCwFnm24lW6qdQLGj58uLZu3aqVK1e6OxS99tprGjduXKHl69atU7Vq1dS2bVvt2LFDmZmZCgoKUkxMjLZs2SJJql+/vux2uw4dOiRJat26tfbs2aP09HRVq1ZNjRs31saNGyVJdevWlaenpw4cOCBJatmypfbv36/U1FT5+fmpefPmWr9+vaTzo1D8/Py0b98+SVJ8fLwOHz6s5ORk+fj4qHXr1vrtt98kSTVr1lRgYKD27Nkj6fzU8cePH9eZM2fk5eWldu3a6bfffpNhGIqIiFBYWJh2794tSWrSpInOnDmjkydPysPDQx06dNC6deuUn5+v6tWrKzIyUjt27JAkNWrUSKmpqTp+/LgkqVOnTtqwYYNyc3MVFham2rVrm9/osbGxOnfunI4dOyZJat++vbZu3aqsrCylpaXp3Llz+v333yVJDRo0UF5eng4fPixJatu2rXbu3Klz584pMDBQsbGx2rx5sySpXr16kqSDBw9Kklq1aqW9e/cqPT1dAQEBatq0qTZs2GBeby8vL+3fv1+S1KJFCx08eFApKSny8/NTfHy81q1bJ0mqVauWAgICtHfvXklS8+bNdfToUZ09e1be3t5q27at1qxZI+n8QxbBwcH6448/zOt94sQJnT59Wp6enmrfvr3Wrl0ru92uiIgIhYeHa9euXZKkxo0b6+zZszp58qRsNps6duyo9evXKy8vT+Hh4YqKijKvd1xcnNLT080HPjp27KhNmzYpJydHoaGhqlu3rrZu3SpJatiwobKyssx3VrRr107btm1TVlaWgoOD1aBBA6d7Nj8/37zebdq00e7du5WRkaHAwEDFxcVp06ZNkqTo6Gh5eHg43bOJiYlKS0uTv7+/mjVrZl7vOnXqyMfHR4mJieb1PnTokJKTk+Xr66uWLVtq7dq15j1brVo183pfccUVSkpK0pkzZwpd78jISIWEhJjXu2nTpjp16pROnTpl3rOO612jRg3VqFFDO3fuNO/ZlJQU84/XBe/Z8PBw1axZU9u3bzfv2YyMDPN6d+jQQVu2bFF2drZCQ0MVHR1t3rMxMTHKycnRkSNHzHuWHOG6HBESEqJ69eqRIy4hR6SnpysjI4McQY4gR5Aj6Ee4OEds3rxZhw9XV05OjrKzs3Xy5ElJ52fAOnYsRWvWnL+3yBHkCHLE5Zkj6Ee4NkekpaWZnxU5ghxBjiBH0I9wfY4omGfJEeQIcgQ5gn6E63NEamqq+W9yRPlzhOP7qDRshlE5xy2MGDFCX3/9tVasWKGYmBhz+dKlS9WzZ0+dPXvWadR4/fr1NXLkSD3++ON68cUXtWDBAvMGl6TExEQ1bNhQGzZsUJs2bdS1a1e1bdtWU6ZMMdvMmDFDI0eONKdvLyg7O1vZ2dnmv1NTUxUdHa2UlBQFBwe79Nwh7dixQ82aNXN3GABQZZFnAcAaJ09K77wjnTx5UhEREU7rbrhB6tjRTYEBQBVEnxYArEWeBQBrkWddIzU1VSEhIaWq2Va6qdQNw9CIESM0b948LV261KkoLp1/GsTb21s//vijuWzXrl06ePCgOnfuLEnq3Lmzfv/9d6fpCxcvXqzg4GBdccUVZpuC+3C0cezjQr6+vgoODnb6gnUaNGjg7hAAoEojzwKANRyPHV/46qeC6wAArkGfFgCsRZ4FAGuRZytepSuMDx8+XLNmzdKnn36qoKAgJSUlKSkpyXzvd0hIiAYPHqwnnnhCy5Yt0/r16/XAAw+oc+fOuvLKKyVJvXr10hVXXKF7771Xmzdv1sKFC/X8889r+PDh8vX1lSQNGzZM+/bt09NPP62dO3dq6tSp+vzzz/X444+77dzxP46pGAAA1iDPAoC1HFPwFURhHABciz4tAFiLPAsA1iLPVrxKVxh/9913lZKSou7du6tWrVrm12effWa2mTx5sm666Sb169dPXbt2Vc2aNTV37lxzvaenp7755ht5enqqc+fOuueee3Tfffdp/PjxZpuYmBh9++23Wrx4sVq1aqWJEyfqgw8+UO/evSv0fAEAAABUHSUVvymMAwAAAAAAuE+lfcd4ZVeW+epRdklJSapZs6a7wwCAKos8CwDWSEqSpk2T0tPTFRgY6LSuVy/pqqvcFBgAVEH0aQHAWuRZALAWedY1/tTvGAckKT8/390hAECVRp4FAGvZ7fZCy3gkGQBciz4tAFiLPAsA1iLPVjwK46iUDh8+7O4QAKBKI88CgDUcxe/U1NRi1wEAXIM+LQBYizwLANYiz1Y8CuMAAAAAUAEojAMAAAAAALgPhXFUSm3atHF3CABQpZFnAcAajuJ3rVq1il0HAHAN+rQAYC3yLABYizxb8SiMo1LavXu3u0MAgCqNPAsA1nAUv0+fPl3sOgCAa9CnBQBrkWcBwFrk2YpHYRyVUkZGhrtDAIAqjTwLANbKyclxdwgAUOXRpwUAa5FnAcBa5NmKR2EclVJgYKC7QwCAKo08CwDWcIwK9/HxKXYdAMA16NMCgLXIswBgLfJsxfO61B1kZGQoOTlZ+fn5Ra6vV6/epR4Cl6G4uDh3hwAAVRp5FgCsFR4eXmgZhXEAcC36tABgLfIsAFiLPFvxyj1ifPr06briiisUHBysevXqKSYmptBXw4YNXRkrLiObNm1ydwgAUKWRZwHAGo7id1JSUrHrAACuQZ8WAKxFngUAa5FnK165Roy/++67Gj58uLy8vNS1a1fVrVtXXl6XPPgcAAAAAP7USip+UxgHAAAAAABwn3JVs6dMmaIaNWpo5cqVaty4satjAhQdHe3uEACgSiPPAoC1QkJCCi2jMA4ArkWfFgCsRZ4FAGuRZyteuaZSP3DggPr3709RHJbx8Cj3LP8AgFIgzwKANRzFb5vNVuw6AIBr0KcFAGuRZwHAWuTZileuK16rVi3l5+e7OhbAdODAAXeHAABVGnkWAKzhKH4nJyfLNytF/plnCq0DALgGfVoAsBZ5FgCsRZ6teOWaSv3+++/XzJkzlZGRoWrVqrk6JgAAAAD40/Kw5+kvm6eqRervkqSjtdpr5dXPyDB83RwZAAAAAADA5atcI8aff/55dejQQddff71WrFih9PR0V8eFy1zLli3dHQIAVGnkWQCwhmFIjXf9R/HJm81ltY+tU/NtnzNiHABcjD4tAFiLPAsA1iLPVrxyFcZ9fX01d+5c/frrr+rRo4dCQkLk6elZ6MvLq1wD0gElJia6OwQAqNLIswBgnTabPlRubp7TsubbKYwDgKvRpwUAa5FnAcBa5NmKV67K9TXXXCObzebqWABTWlqau0MAgCqNPAsA1nAUv+12u3sDAYDLAH1aALAWeRYArEWerXjlKowvX77cxWEAzvz9/d0dAgBUaeRZALCGozBu8yj8IDEjxgHAtejTAoC1yLMAYC3ybMUr11TqgNWaNWvm7hAAoEojzwKAtXx8fAotozAOAK5FnxYArEWeBQBrkWcr3iUXxo8cOaJvv/1Ws2fP1rfffqsjR464Ii5c5jZs2ODuEACgSiPPAoA1HMXv7KzsYtcBAFyDPi0AWIs8CwDWIs9WvHJNpS5Je/bs0SOPPKKlS5cWWtezZ09NnTpVcXFxlxQcAAAAAPyZlFT8pjAOAAAAAADgPuUqjB86dEhXX321Tpw4oaZNm6pr166qVauWkpKStGLFCi1ZskTXXHONfvvtN0VHR7s6ZlwG6tSp4+4QAKBKI88CgLW8vAr/qkVhHABciz4tAFiLPAsA1iLPVrxyFcbHjRunEydOaOrUqRo6dKhsNpvT+v/7v//TI488ovHjx+v99993SaC4vBT1TkYAgOuQZwHAGo7i94W/IxVcBwBwDfq0AGAt8iwAWIs8W/HK9Y7xhQsX6uabb9awYcOK/IPP0KFDdfPNN+v777+/5ABxeUpMTHR3CABQpZFnAcBaubm5hZZRGAcA16JPCwDWIs8CgLXIsxWvXIXxEydOKD4+vsQ28fHxOnnyZLmCAgAAAIA/I94xDgAAAAAAUDmVqzAeERGh7du3l9hm+/btioiIKFdQQIsWLdwdAgBUaeRZALCGo/jt61t4OjQK4wDgWvRpAcBa5FkAsBZ5tuKVqzDeu3dvLViwQNOnTy9y/Ycffqj//Oc/+stf/nJJweHydejQIXeHAABVGnkWAKyVm5dXaBmFcQBwLfq0AGAt8iwAWIs8W/G8yrPRmDFj9J///EdDhgzRlClT1K1bN0VFRen48eNasWKFtm3bpho1amjMmDGujheXieTkZHeHAABVGnkWAKxhGJJNkj3f7u5QAKDKo08LANYizwKAtcizFa9chfF69erpl19+0dChQ7V8+XJt27bNaX2PHj307rvvKjo62iVB4vLj6+vr7hAAoEojzwKAtWw2W6FljBgHANeiTwsA1iLPAoC1yLMVr1yFcUlq1KiRli5dqkOHDmnTpk1KTU1VcHCwWrduTUEcl6xly5buDgEAqjTyLABY43/vGC/8yy2FcQBwLfq0AGAt8iwAWIs8W/HK9Y7xgqKjo3XzzTfr7rvv1s0330xRHC6xdu1ad4cAAFUaeRYArOEofmdlZRW7DgDgGvRpAcBa5FkAsBZ5tuJdcmEcAAAAAHBxFMYBAAAAAADcp1RTqT/44IOy2Wx69dVXFRUVpQcffLBUO7fZbJo+ffolBYjLU82aNd0dAgBUaeRZALCGo/jt6eVZ7DoAgGvQpwUAa5FnAcBa5NmKV6rC+MyZM2Wz2fSPf/xDUVFRmjlzZql2TmEc5VWtWjV3hwAAVRp5FgCs4Sh+e3gUnpyLwjgAuBZ9WgCwFnkWAKxFnq14pSqMJyYmSpLq1Knj9G/AKnv37lWNGjXcHQYAVFnkWQCwVm5Orjz9nUeNUxgHANeiTwsA1iLPAoC1yLMVr1SF8fr165f4bwAAAABAycVvCuMAAAAAAADuU3h+v1IYP368VqxYUWKbn3/+WePHjy9XUMAVV1zh7hAAoEojzwKAtXx8fAotozAOAK5FnxYArEWeBQBrkWcrXrkK42PHjtXy5ctLbLNixQqNGzeuPLsHlJSU5O4QAKBKI88CgDUcxe+8/Lxi1wEAXIM+LQBYizwLANYiz1a8chXGSyMnJ0eenp4XbwgU4cyZM+4OAQCqNPIsAFjDUfy259vdGwgAXAbo0wKAtcizAGAt8mzFK3dh3GazFbsuJydHP//8syIjI8u7e1zmvL293R0CAFRp5FkAsFZRvy4xYhwAXIs+LQBYizwLANYiz1Y8r9I2bNiwodO/J0+erBkzZhRql5+fr1OnTikrK0sPP/zwpUeIy1Lbtm3dHQIAVGnkWQCwhqP47evnV+w6AIBr0KcFAGuRZwHAWuTZilfqEeN2u12GYcgwDNlsNvP/L/zy9vZW8+bNNWrUKE2cONHK2FGFrVmzxt0hAECVRp4FAGtlZWYVWkZhHABciz4tAFiLPAsA1iLPVrxSjxjfv3+/+f8eHh56/PHH9eKLL1oREwAAAAD8KZVU/KYwDgAAAAAA4D6lLowXlJiYqNDQUBeHAvwP76cHAGuRZwHAGo7it6enZ+F1dkNSES8fBwCUC31aALAWeRYArEWerXilnkq9oNDQUB04cEDnzp0rcn1GRoa2bNmi1NTUSwoOl6+QkBB3hwAAVRp5FgCs5eFZ+Fet84VxAICr0KcFAGuRZwHAWuTZileuwvj48ePVpUsX5efnF7k+Pz9fXbp00SuvvHJJweHy9ccff7g7BACo0sizAGANx4jx3Jzcwivt9ooNBgCqOPq0AGAt8iwAWIs8W/HKVRj/4YcfdP311ysoKKjI9cHBwerdu7e+++67SwoOAAAAAP5MeMc4AAAAAABA5VSuwvjBgwfVqFGjEtvExsbq4MGD5QoKaNq0qbtDAIAqjTwLANby8fEpvJAR4wDgUvRpAcBa5FkAsBZ5tuKVqzBus9mUnZ1dYpvs7Oxip1oHLubUqVPuDgEAqjTyLABYwzEqvMjfhRgyDgAuRZ8WAKxFngUAa5FnK165CuNNmzbVDz/8IKOYP+zY7XZ9//33atKkySUFh8sXyQAArEWeBQBrURgHAOvRpwUAa5FnAcBa5NmKV67C+J133qndu3frwQcfVEpKitO6lJQUPfjgg9qzZ4/uuecelwSJy4+HR7luTQBAKZFnAcAaJb5j3E5hHABciT4tAFiLPAsA1iLPVjybUdyw7xLk5uaqR48eWrVqlUJDQ9WhQwfVqVNHR44c0dq1a5WcnKyuXbtq8eLF8vb2tiJut0tNTVVISIhSUlIUHBzs7nAAAAAAVAK//iqF3X9zketWPDJHD4+sVsERAQAAAAAAVF1lqdmW61EEb29vLVmyRE888YTy8/O1ePFizZw5U4sXL5bdbteoUaO0cOHCKlsUh/XWrl3r7hAAoEojzwKAtbIyswotY8Q4ALgWfVoAsBZ5FgCsRZ6teF7l3dDPz09vvvmm/vnPf2rnzp1KSUlRaGiomjRpIk9PT1fGiMuQ3W53dwgAUKWRZwHAGiUWv3nHOAC4FH1aALAWeRYArEWerXjlLow7eHp6qnnz5oWWr127VtOnT9e0adMu9RC4DNWoUcPdIQBAlUaeBQBrOArjRT4szC+8AOBS9GkBwFrkWQCwFnm24rn0re5nzpzRW2+9pZYtW+rKK6/U+++/78rd4zJCMgAAa5FnAcAi/y1+F1UYZ8A4ALgWfVoAsBZ5FgCsRZ6teC4pjC9cuFADBgxQnTp19MQTT2jr1q3q3Lmz3nvvPVfsHpehnTt3ujsEAKjSyLMAYJH/Vr9zcnIKr2PEOAC4FH1aALAWeRYArEWerXjlnkr9wIED+vDDDzVz5kwdPnxYxn//ANSlSxdNnz5djRs3dlmQAAAAAPCnUMKw8BLfPw4AAAAAAABLlWnEeE5OjubMmaPrr79esbGxeumll3T69Gndeeed+uGHHyRJzZo1u6Si+IoVK3TzzTerdu3astlsmj9/vtP6QYMGyWazOX395S9/cWpz5swZ3X333QoODlZoaKgGDx6s9PR0pzZbtmzRNddcIz8/P0VHR+uNN94od8xwvUaNGrk7BACo0sizAGANR/Hb28e78EpGjAOAS9GnBQBrkWcBwFrk2YpX6hHjf//73/Xpp58qOTlZktS9e3fde++9uv322xUYGOiygDIyMtSqVSs9+OCDuu2224ps85e//EUzZsww/+3r6+u0/u6779axY8e0ePFi5ebm6oEHHtCQIUP06aefSpJSU1PVq1cvXXfddZo2bZp+//13PfjggwoNDdWQIUNcdi4ov5SUFIWHh7s7DACossizAGANR2Hcnm8v8j3jAADXoU8LANYizwKAtcizFa/UhfF33nlHHh4eGjlypB5//HHVrVvXkoD69OmjPn36lNjG19dXNWvWLHLdjh079MMPP2jt2rVq3769JOntt9/WDTfcoDfffFO1a9dWQkKCcnJy9OGHH8rHx0fNmzfXpk2bNGnSJArjlcSJEycUExPj7jAAoMoizwKANWzG+VHh+fn58tYFo8YZMQ4ALkWfFgCsRZ4FAGuRZyteqadSDwwMlN1u17///W+NGDFCc+fOVU5OjpWxFWv58uWKjIxUkyZN9Mgjj+j06dPmutWrVys0NNQsikvSddddJw8PD61Zs8Zs07VrV/n4+JhtevfurV27duns2bNFHjM7O1upqalOXwAAAABQUInvES/h/eMAAAAAAACwVqlHjCclJemzzz7T9OnTtWDBAv3nP/9RaGio+vfvr/vuu0+dO3e2Mk7TX/7yF912222KiYnR3r179eyzz6pPnz5avXq1PD09lZSUpMjISKdtvLy8FB4erqSkJPNcLnwCIyoqylwXFhZW6Livvfaaxo0bV2j5unXrVK1aNbVt21Y7duxQZmamgoKCFBMToy1btkiS6tevL7vdrkOHDkmSWrdurT179ig9PV3VqlVT48aNtXHjRklS3bp15enpqQMHDkiSWrZsqf379ys1NVV+fn5q3ry51q9fL0mqXbu2/Pz8tG/fPklSfHy8Dh8+rOTkZPn4+Kh169b67bffJEk1a9ZUYGCg9uzZI+n8u+CPHz+uM2fOyMvLS+3atdNvv/0mwzAUERGhsLAw7d69W5LUpEkTnTlzRidPnpSHh4c6dOigdevWKT8/X9WrV1dkZKR27Ngh6fz7EFJTU3X8+HFJUqdOnbRhwwbl5uYqLCxMtWvX1rZt2yRJsbGxOnfunI4dOyZJat++vbZu3aqsrCyFhITo3Llz+v333yVJDRo0UF5eng4fPixJatu2rXbu3Klz584pMDBQsbGx2rx5sySpXr16kqSDBw9Kklq1aqW9e/cqPT1dAQEBatq0qTZs2GBeby8vL+3fv1+S1KJFCx08eFApKSny8/NTfHy81q1bJ0mqVauWAgICtHfvXklS8+bNdfToUZ09e1be3t5q27at+fBFVFSUgoOD9ccff5jX+8SJEzp9+rQ8PT3Vvn17rV27Vna7XREREQoPD9euXbskSY0bN9bZs2d18uRJ2Ww2dezYUevXr1deXp7Cw8MVFRVlXu+4uDilp6eb93bHjh21adMm5eTkKDQ0VHXr1tXWrVslSQ0bNlRWVpaOHj0qSWrXrp22bdumrKwsBQcHq0GDBk73bH5+vnm927Rpo927dysjI0OBgYGKi4vTpk2bJEnR0dHy8PBwumcTExOVlpYmf39/NWvWzLzederUkY+PjxITE83rfejQISUnJ8vX11ctW7bU2rVrzXu2WrVq5vW+4oorlJSUpDNnzhS63pGRkQoJCTGvd9OmTXXq1CmdOnXKvGcd17tGjRqqUaOGdu7cad6zKSkpOnHiRKF7Njw8XDVr1tT27dvNezYjI8O83h06dNCWLVuUnZ2t0NBQRUdHm/dsTEyMcnJydOTIEfOeJUe4NkfUq1ePHHGJOSIjI4McQY4gR5Aj6Ee4OEfs+WO3gjKz5OHpIXu+3XyY2NvHW8lnTmvNmvP3CzmCHEGOuDxzBP0I1+aIZs2amZ8VOYIcQY4gR9CPcH2OaNKkibktOYIcQY4gR9CPcH2OaNSokflvckT5c4Tj+6g0bIZR9mELu3fv1gcffKBPPvlEx48fl81mU8OGDbVv3z4NGjRI06dPL+suiw7OZtO8efPUt2/fYtvs27dPsbGxWrJkiXr27KlXX31VH330kfnN6BAZGalx48bpkUceUa9evRQTE6P/+7//M9dv375dzZs31/bt29WsWbNCx8nOzlZ2drb579TUVEVHRyslJUXBwcGXfrJwsmHDBrVt29bdYQBAlUWeBQBrrPw+TREj71J2VpZ8/fyc1i0d8J4eGV/LTZEBQNVDnxYArEWeBQBrkWddIzU1VSEhIaWq2ZZ6KvWCGjdurDfeeEOHDx/W3Llz1adPH+3fv1+GYWjmzJm69tpr9cknn+jcuXPlOoGyaNiwoWrUqGE+MVSzZk3zCQmHvLw8nTlzxnwvec2aNc0nfxwc/y7u3eW+vr4KDg52+oJ1cnNz3R0CAFRp5FkAsIaRf/494kU+fsw7xgHApejTAoC1yLMAYC3ybMUrV2HcwdPTU3379tU333yjgwcP6uWXX1bDhg21fPlyDRo0SLVqWT8a4vDhwzp9+rR5rM6dOys5Odkcci9JS5culd1uV6dOncw2K1ascLrhFi9erCZNmhQ5jToqXnh4uLtDAIAqjTwLANby8Cz8q1aJ7x8HAJQZfVoAsBZ5FgCsRZ6teJdUGC+oVq1aevbZZ/XHH39o2bJluuuuu5SXl1fm/aSnp2vTpk3mPP+JiYnatGmTDh48qPT0dI0aNUq//vqr9u/frx9//FG33nqr4uLi1Lt3b0nn33nwl7/8RQ8//LB+++03/fLLLxoxYoQGDhyo2rVrS5Luuusu+fj4aPDgwdq2bZs+++wzvfXWW3riiSdcdTlwiYobuQ8AcA3yLABY5L+jwr08vQqvK/tbrAAAJaBPCwDWIs8CgLXIsxXPZYXxgrp166ZPPvlEx44dK/O269atU5s2bdSmTRtJ0hNPPKE2bdroxRdflKenp7Zs2aJbbrlFjRs31uDBg9WuXTv9/PPP8vX1NfeRkJCgpk2bqmfPnrrhhht09dVX67333jPXh4SEaNGiRUpMTFS7du305JNP6sUXX9SQIUMu/eThEtu3b3d3CABQpZFnAcAajlHhOTk5hVcylToAuBR9WgCwFnkWAKxFnq14RQxjcJ3yvIe7e/fuMkoYSbFw4cKL7iM8PFyffvppiW1atmypn3/+uczxAQAAAEBxbEYJxW9GjAMAAAAAALiNJSPGgUsVGxvr7hAAoEojzwKANRy1b28f78LreMc4ALgUfVoAsBZ5FgCsRZ6teBTGUSllZGS4OwQAqNLIswBgkf9Ol24vYtp0myiMA4Ar0acFAGuRZwHAWuTZikdhHJVSUlKSu0MAgCqNPAsAFvnvkPH8vPzC63jHOAC4FH1aALAWeRYArEWerXgUxgEAAADARYz84ovfBu8YBwAAAAAAcBsK46iUOnTo4O4QAKBKI88CgLX8/PwKL+Qd4wDgUvRpAcBa5FkAsBZ5tuJRGEeltGXLFneHAABVGnkWACzy3+nSs7OzC69jxDgAuBR9WgCwFnkWAKxFnq14XuXd8OTJk5oxY4bWrl2r5ORk5ecXfoeezWbTjz/+eEkB4vJU5B8SAQAuQ54FAGsY/x0VXuS06bxjHABcij4tAFiLPAsA1iLPVrxyFca3bNmia6+9VmfPni3xPXk2m63cgeHyFhoa6u4QAKBKI88CgDVsOv/7kYdn4cm5GDAOAK5FnxYArEWeBQBrkWcrXrmmUn/yySd15swZPffcc0pMTFRubq7sdnuhr6JGkQOlER0d7e4QAKBKI88CgDUcI8a9vQo/g2wz7BTHAcCF6NMCgLXIswBgLfJsxStXYXz16tXq27evxo8fr/r168vT09PVceEy9/vvv7s7BACo0sizAGANm+F4x3hO4XUyKIwDgAvRpwUAa5FnAcBa5NmKV67CuI+Pj2JjY10dCwAAAAD8qTlGjBeFEeMAAAAAAADuU67CeLdu3bRu3TpXxwKYYmJi3B0CAFRp5FkAsIY5lbq3dxErGTEOAK5EnxYArEWeBQBrkWcrXrkK42+++aa2bt2qN99809XxAJKknJzCU08CAFyHPAsA1rDpfOXbKKIC7lgHAHAN+rQAYC3yLABYizxb8bzKs9Err7yi+Ph4/eMf/9C0adPUunVrBQcHF2pns9k0ffr0Sw4Sl58jR46obt267g4DAKos8iwAWMPIP/+O8by8PHl5X/DrFiPGAcCl6NMCgLXIswBgLfJsxStXYXzmzJnm/+/bt0/79u0rsh2FcQAAAACXk5JGhfOOcQAAAAAAAPcpV2E8MTHR1XEATtq2bevuEACgSiPPAoA1HO8Y9/XzLbTOJkaMA4Ar0acFAGuRZwHAWuTZileuwnj9+vVdHQfgZMeOHWrZsqW7wwCAKos8CwAW+W/lOycnR76+voXWURgHANehTwsA1iLPAoC1yLMVz8PdAQBFyczMdHcIAFCl/X97dx4nR13nf/xd1efck0kyM5nc5CaBhJyynGJWRERRll0VBZGFlQ0o4s8LkeMnh+KFIIi4uuhvPRD3AQKeCAjIEXKQhIRc5CDnZJLMffRVVb8/erqn557MdHXP9Lyej0dnqquqK9+u6fn0t+rzPYizAOCS9sx3oud4KnqMA0B6UacFAHcRZwHAXcTZzBtQj/EXX3xRkrR8+XIFg8Hk84E4++yzB1cyjGpFRUXZLgIA5DTiLAC4xLYlSabZQxtkeowDQFpRpwUAdxFnAcBdxNnMG1Bi/Nxzz5VhGNq6datmz56dfD4QlmUNqYAYnaZPn57tIgBATiPOAoA7Ej3Ffb7ul1qGY5MYB4A0ok4LAO4izgKAu4izmTegxPgtt9wiwzA0bty4Ts8Bt2zatEkrVqzIdjEAIGcRZwHAHYYT7zEeDkcUzAt23sZQ6gCQVtRpAcBdxFkAcBdxNvMGlBi/7bbb+nwOAAAAAFDfiW+GUgcAAAAAAMiaHia+A7Jv6tSp2S4CAOQ04iwAuCPRY7zHodTpMQ4AaUWdFgDcRZwFAHcRZzOPxDiGJdu2s10EAMhpxFkAcEdijvGe8t+JpDkAID2o0wKAu4izAOAu4mzmkRjHsLR///5sFwEAchpxFgBc0n5RG4vGetxMj3EASB/qtADgLuIsALiLOJt5JMYBAAAAIAMMxyYxDgAAAAAAkCUkxjEsLVq0KNtFAICcRpwFAJe09xgPBALdtznMMQ4A6USdFgDcRZwFAHcRZzOPxDiGpbfffjvbRQCAnEacBQB3JOYYj0Yj3bbRYxwA0os6LQC4izgLAO4izmYeiXEMS83NzdkuAgDkNOIsALjDUDzzbdvdM+CG6DEOAOlEnRYA3EWcBQB3EWczzzvUA7z11lvatm2bWlpa9MlPfjIdZQJUUFCQ7SIAQE4jzgKAOxI9xk3T6GEjiXEASCfqtADgLuIsALiLOJt5g+4xvmbNGi1atEinnHKKLr30Un3qU59KbnvxxReVn5+vJ598Mh1lxCg0e/bsbBcBAHIacRYA3GE48TnGfX5/9230GAeAtKJOCwDuIs4CgLuIs5k3qMT4li1bdN5552nPnj36/Oc/rwsuuKDT9rPOOkvjxo3TY489lpZCYvR54403sl0EAMhpxFkAcEeix3g4FO62jTnGASC9qNMCgLuIswDgLuJs5g0qMX7rrbdKktatW6fvfOc7WrZsWafthmHo9NNP15o1a4ZeQgAAAAAYKfrKfDOUOgAAAAAAQNYMKjH+wgsv6JJLLtHMmTN73WfKlCk6fPjwoAuG0W3SpEnZLgIA5DTiLAC4pD3z7fV6u21iKHUASC/qtADgLuIsALiLOJt5g0qMNzU1qby8vM992traZFnWoAoFeDyebBcBAHIacRYAXNKe+TaM3rcBANKDOi0AuIs4CwDuIs5m3qAS45MnT9abb77Z5z7r16/XjBkzBlUo4J133sl2EQAgpxFnAcAdhmNLkqLRWI/byI0DQPpQpwUAdxFnAcBdxNnMG1Ri/AMf+ID++te/6m9/+1uP23/729/qtdde08UXXzyUsgEAAADAiOLYvWe+GUodAAAAAAAge7pPfDcAN910k373u9/p/e9/v6644gpVV1dLkh588EG9+uqr+vWvf61p06bpxhtvTGthMXqceuqp2S4CAOQ04iwAuKQ98x0I+HvcRmIcANKHOi0AuIs4CwDuIs5m3qB6jI8fP14vvPCCli1bpp/+9Kf6wx/+IMdxdN111+mXv/ylli1bpueee04lJSXpLi9Gib1792a7CACQ04izAOCS9sx3j0Op02McANKKOi0AuIs4CwDuIs5m3qB6jEvSSSedpJdfflkbNmzQa6+9ptraWhUXF2vFihVatmxZOsuIUaixsTHbRQCAnEacBQCX2Hb7D7v7NnqMA0BaUacFAHcRZwHAXcTZzBt0Yjxh0aJFWrRoURqKAnQIBoPZLgIA5DTiLAC4pD3zbRhGt02GY5MYB4A0ok4LAO4izgKAu4izmTeoodQBt82fPz/bRQCAnEacBQCX9DHHOEOpA0B6UacFAHcRZwHAXcTZzBtUj/FPf/rT/e5jmqaKi4s1Z84cfeADH9DEiRMH819hlFq3bp1WrFiR7WIAQM4izgKAS9oz36FQWMG8YLdtJMYBIH2o0wKAu4izAOAu4mzmDSox/sgjjySHBnR6uLNjGEan9ddff71uueUW3XzzzYMsJgAAAACMAE4Pc4u3o8c4AAAAAABA9gxqKPVdu3bpAx/4gMrLy3XXXXfphRde0LZt2/TCCy/orrvuUkVFhT74wQ9q9erVevjhh1VVVaVbb71Vjz76aLrLjxxVVVWV7SIAQE4jzgKAS+x45tvr9XTbxBzjAJBe1GkBwF3EWQBwF3E28wbVY/zRRx/V6tWrtXHjRlVUVCTXz549W2eddZY+9alPadGiRXr++ef1pS99SRdccIFOPvlkPfjgg/q3f/u3tBUeuSsYDPa/EwBg0IizAOCS9sy3YfTQBpmsOACkFXVaAHAXcRYA3EWczbxB9Rj/6U9/qn/913/tlBRPVVlZqUsvvVQ/+clPJEkTJ07UBz7wAW3cuHHwJcWosnv37mwXAQByGnEWAFzSnvyORqPdNjGUOgCkF3VaAHAXcRYA3EWczbxBJcYPHDigQCDQ5z7BYFAHDhxIPp8yZYpCodBg/jsAAAAAGBns3ucYl0NiHAAAAAAAIFsGlRifOHGinnjiiV4T3aFQSE888YQmTpyYXFdTU6MxY8YMrpQYdRYsWJDtIgBATiPOAoBL2jPf/oC/2ybmGAeA9KJOCwDuIs4CgLuIs5k3qMT4VVddpV27dunMM8/Uk08+qePHj0uSjh8/rieffFJnnnmmdu/erU9/+tPJ17z00ktauHBhekqNnJc62gAAIP2IswDgkvbMdywW67aJodQBIL2o0wKAu4izAOAu4mzmeQfzoi996UvaunWr/ud//kcf/vCHJUmmacpuHzbQcRx9/OMf11e+8hVJ0pEjR3ThhRfqfe97X5qKjVxXX1+f7SIAQE4jzgKAS9oz37bVw5DqDKUOAGlFnRYA3EWcBQB3EWczb1CJcY/Ho1/84hf61Kc+pf/3//6fNm3apMbGRhUXF2vhwoW67LLL9J73vCe5f0VFhb7//e+nrdDIfX5/96EnAQDpQ5wFAHcYTjwhbhg9bKPHOACkFXVaAHAXcRYA3EWczbxBJcYTzjvvPJ133nnpKguQtGjRomwXAQByGnEWANzhtGe+A4Fgt23MMQ4A6UWdFgDcRZwFAHcRZzNvUHOMA257/fXXs10EAMhpxFkAcIkdz3yHQqHu2xhKHQDSijotALiLOAsA7iLOZt6QeoxXV1dr3bp1qq+vl2VZPe5z+eWXD+W/AAAAAIARw1DvmW+GUgcAAAAAAMieQSXGQ6GQrr76av3mN7+Rbds97uM4jgzDIDGOQamsrMx2EQAgpxFnAcAdTnuPcY/X08NGEuMAkE7UaQHAXcRZAHAXcTbzBpUY/8pXvqJf/vKXmj17tj72sY9p0qRJ8nqH1Pkc6KSwsDDbRQCAnEacBQB3GE684bBpdp+1qq/e5ACAE0edFgDcRZwFAHcRZzNvUHOM//a3v9XJJ5+sjRs36tZbb9VVV12lK664osfHiXrxxRd10UUXqaqqSoZh6Iknnui03XEc3XLLLZowYYLy8vK0cuVK7dy5s9M+tbW1uuyyy1RcXKzS0lJdddVVam5u7rTPpk2bdNZZZykYDGry5Mm65557TriscM/bb7+d7SIAQE4jzgKAS9q7hEcj0W6bDMemxzgApBF1WgBwF3EWANxFnM28QSXG6+vr9b73vU+BQCDd5VFLS4sWLlyoBx54oMft99xzj+677z499NBDWr16tQoKCnT++ecrFAol97nsssu0ZcsWPfPMM3r66af14osv6pprrklub2xs1Hvf+15NnTpV69at07e//W3ddtttevjhh9P+fgAAAACMIn1lvhlKHQAAAAAAIGsGNf75nDlzdOTIkXSXRZJ0wQUX6IILLuhxm+M4uvfee3XzzTfrQx/6kCTpF7/4hSoqKvTEE0/oox/9qLZu3ao///nPWrNmjZYuXSpJuv/++/X+979f3/nOd1RVVaVf/vKXikQi+tnPfia/36/58+drw4YN+t73vtcpgY7smTdvXraLAAA5jTgLAC5pz3z7/f5umwyRGAeAdKJOCwDuIs4CgLuIs5k3qB7jX/ziF/X73/8+41389+zZo+rqaq1cuTK5rqSkRCtWrNCrr74qSXr11VdVWlqaTIpL0sqVK2WaplavXp3c5+yzz+50s+r888/X9u3bVVdXl6F3g7641fACABBHnAUAl9jxOcZjVqz7NnqMA0BaUacFAHcRZwHAXcTZzBtUj/FJkybp/PPP1/Lly3XDDTdo8eLFKi4u7nHfs88+e0gFTFVdXS1Jqqio6LS+oqIiua26ulrl5eWdtnu9XpWVlXXaZ/r06d2Okdg2ZsyYbv93OBxWOBxOPm9sbBziu0Ffamtrs10EAMhpxFkAcEl75tu27G6bmGMcANKLOi0AuIs4CwDuIs5m3qAS4+eee64Mw5DjOLrttttkGEav+1qWNejCDSd33323br/99m7r165dq4KCAi1evFhbt25VW1ubioqKNH36dG3atEmSNHXqVNm2rf3790uSFi1apLffflvNzc0qKCjQ7Nmz9cYbb0iKNzrweDx65513JEmnnnqq9u7dq8bGRgWDQc2fP1/r1q2TJFVVVSkYDGr37t2SpAULFujAgQOqr6+X3+/XokWL9Prrr0uSKisrVVhYmOzlP2/ePB05ckS1tbXyer1asmSJXn/9dTmOo/Hjx2vMmDHasWOHpPjQ+bW1tTp69KhM09SyZcu0du1aWZalsWPHqry8XFu3bpUkzZo1S42NjclWLitWrND69esVjUY1ZswYVVVVacuWLZKkGTNmqLW1VYcPH5YkLV26VJs3b1YoFFJLS4taW1v15ptvSpKmTZumWCymAwcOSJIWL16sbdu2qbW1VYWFhZoxY4Y2btwoSZoyZYokad++fZKkhQsXateuXWpublZ+fr7mzp2r9evXJ8+31+vV3r17JUmnnHKK9u3bp4aGBgWDQS1YsEBr166VJE2YMEH5+fnatWuXJGn+/Pk6dOiQ6urq5PP5tHjx4uSoBBUVFSouLtbOnTuT57umpkbHjx+Xx+PR0qVLtWbNGtm2rfHjx6usrEzbt2+XJM2ePVt1dXU6evSoDMPQ8uXLtW7dOsViMZWVlamioiJ5vmfOnKnm5uZko4/ly5drw4YNikQiKi0t1aRJk7R582ZJ0kknnaRQKKRDhw5JkpYsWaItW7YoFAqpuLhY06ZN6/SZtSwreb5PO+007dixQy0tLSosLNTMmTO1YcMGSdLkyZNlmmanz+yePXvU1NSkvLw8zZs3L3m+J06cKL/frz179iTP9/79+1VfX69AIKBTTz1Va9asSX5mCwoKkuf75JNPVnV1tWpra7ud7/LycpWUlCTP99y5c3Xs2DEdO3Ys+ZlNnO9x48Zp3Lhx2rZtW/Iz29DQoJqamm6f2bKyMlVWVuqtt95KfmZbWlqS53vZsmXatGmTwuGwSktLNXny5ORndvr06YpEIjp48GDyM0uMSF+MKCkp0ZQpU4gRQ4gRra2tamlpIUYQI4gRxAjqEWmOEbXHj2lsW0i2Y8u2bEUiEUmSz+9TqK1Vmza9qUiklRhBjCBGjNIYQT0ivTHCcZzk74oYQYwgRhAjqEekP0bYtp18LTGCGEGMIEZQj0h/jLAsK/mcGDH4GJH4OxoIw3FOvM9Cf8nwVLfeeuuJHj7JMAw9/vjjuvjiiyVJu3fv1owZM/TGG29o0aJFyf3OOeccLVq0SD/4wQ/0s5/9TF/4whc6DYkei8UUDAb12GOP6cMf/rAuv/xyNTY26oknnkju8/zzz+u8885TbW3tgHuMT548WQ0NDb32lgcAAAAwujz3iZ9p4prHe9y2e/pKld/5OS1ZkuFCAQAAAAAA5KjGxkaVlJQMKGc7qB7jt91222BeNmTTp09XZWWlnn322WRivLGxUatXr9a1114rSTr99NNVX1+vdevWaUn7HafnnntOtm1rxYoVyX2+9rWvKRqNyufzSZKeeeYZzZkzp8ekuCQFAgEFAgGX3yESXn/9dS1fvjzbxQCAnEWcBQB3GE58CPVQW0jBvGDnbWKOcQBIJ+q0AOAu4iwAuIs4m3lmtgvQVXNzszZs2JAczmDPnj3asGGD9u3bJ8MwdMMNN+iOO+7Qk08+qTfffFOXX365qqqqkr3K582bp/e97326+uqr9frrr+vll1/Wddddp49+9KOqqqqSJH384x+X3+/XVVddpS1btujRRx/VD37wA914441ZetfoahADGQAATgBxFgBc0kd8ZY5xAEgv6rQA4C7iLAC4izibeYPqMe6mtWvX6t3vfnfyeSJZfcUVV+iRRx7Rl770JbW0tOiaa65RfX29zjzzTP35z39WMNjRG+OXv/ylrrvuOr3nPe+RaZq65JJLdN999yW3l5SU6K9//atWrVqlJUuWaNy4cbrlllt0zTXXZO6Nok/jx4/PdhEAIKcRZwHAJe0XtR6Pp9dtAID0oE4LAO4izgKAu4izmTeoOcYlaf/+/brjjjv0t7/9TYcOHVIkEul+cMNQLBYbciGHoxMZrx4nrq6urtdh7QEAQ0ecBQB3PP/RH6vqjadlWVa35Pg7U85W6Z1fFKOkAUB6UKcFAHcRZwHAXcTZ9DiRnO2ghlLfvXu3Fi9erJ/+9KcqLCxUOBzWlClTNHv2bHm9XjmOo1NPPVVnnXXWoN4AsGPHjmwXAQByGnEWAFxix+cYj0aiPW6m0zgApA91WgBwF3EWANxFnM28QSXGb7/9djU0NOjZZ5/Vxo0bJUlXXnmltm7dqr179+qDH/ygWlpa9Lvf/S6thQUAAACA4cwQc4wDAAAAAAAMR4NKjP/tb3/T+9//fp1zzjnJdYkR2SdMmKBHH31UknTTTTeloYgYjebMmZPtIgBATiPOAoBL2q+L/H5/t02GHBLjAJBG1GkBwF3EWQBwF3E28waVGD927Jjmzp2bfO71etXa2pp8HggE9M///M96+umnh15CjEq1tbXZLgIA5DTiLAC4I9Fg2LKsnjaSGAeANKJOCwDuIs4CgLuIs5k3qMT4uHHj1NLS0un53r17O+3j9XpVX18/lLJhFDt69Gi2iwAAOY04CwDuMNrnGO8pMU6PcQBIL+q0AOAu4iwAuIs4m3mDSozPmjVLu3btSj5fvny5/vKXv2j37t2S4r/I3/3ud5oxY0Z6SolRxzQH9dEEAAwQcRYAXNJH5ps5xgEgvajTAoC7iLMA4C7ibOYN6oxfcMEFev7555M9wm+44QY1NTXp1FNP1bJlyzR79mxVV1fr+uuvT2dZMYosW7Ys20UAgJxGnAUAl7RnvoN5wR63kRgHgPShTgsA7iLOAoC7iLOZN6jE+LXXXqu///3v8ng8kqRzzz1Xv/nNbzR16lRt3rxZFRUVuu+++3T11VentbAYPdauXZvtIgBATiPOAoBb4pnvUCjUbQtDqQNAelGnBQB3EWcBwF3E2czzDuZFxcXFWrFiRad1l156qS699NK0FAroaU5GAED6EGcBwCWJzHdPCXB6jANAWlGnBQB3EWcBwF3E2cxj8HoMS2PHjs12EQAgpxFnAcAdhm1LUnJ0rU7besyWAwAGizotALiLOAsA7iLOZt6geownxGIxbd++XfX19b22ajj77LOH8l9glCovL892EQAgpxFnAcAl7V3Ce0yMOzY9xgEgjajTAoC7iLMA4C7ibOYNKjHuOI5uueUW3X///WpqaupzX4YBwGBs3bq123D9AID0Ic4CgEvaM9+RSETBvGC3bSTGASB9qNMCgLuIswDgLuJs5g0qMf6Nb3xDd955p0pLS3X55Zdr0qRJ8nqH1PkcAAAAAEa8voZLp8c4AAAAAABA9gwqm/2zn/1MU6dO1dq1axn/Hq6YNWtWtosAADmNOAsALmmfY9zn9/W4mcQ4AKQPdVoAcBdxFgDcRZzNPHMwL6qurtbFF19MUhyuaWxszHYRACCnEWcBwB2JvLfdniBPRY9xAEgv6rQA4C7iLAC4izibeYNKjE+fPp1fFlx15MiRbBcBAHIacRYA3GE48YS4FbO6bxNzjANAOlGnBQB3EWcBwF3E2cwbVGL82muv1dNPP62ampp0lwcAAAAARi6bOcYBAAAAAACGowHNMb5v375Ozz/0oQ/ppZde0j/90z/plltu0eLFi1VcXNzja6dMmTL0UiLn2bb017/Gf1qWZNsr9Pjj0qxZ0oIF2S4dAOSeFStWZLsIAJCTEj3Gg3nBHrf3MMI6AGCQqNMCgLuIswDgLuJs5g0oMT5t2jQZhtFtveM4uvLKK3t9nWEYisVigy8dRpXXXutYPnz4sCZMmKCCAhLjAOCG9evXa/HixdkuBgDkrHAopECwc3LccGwS4wCQRtRpAcBdxFkAcBdxNvMGlBi//PLLe0yMA+nS9eNlWfE5GblxCADuiEaj2S4CAOSm9gpsj0OmOw71WwBII+q0AOAu4iwAuIs4m3kDSow/8sgjLhcDo51hSKbZkQjPy8uTRGIcANwyZsyYbBcBAHKSoXhG3PSYPW6jfgsA6UOdFgDcRZwFAHcRZzOv+90aIEvMlE9jUVGRJBLjAOCWqqqqbBcBAHJTewXW6+2hDTI9xgEgrajTAoC7iLMA4C7ibOadUGL8zjvv1E033dRn1/5IJKKbbrpJ3/zmN4dcOIwuqYnxmpoaSSTGAcAtW7ZsyXYRACCnRcKRbuuYYxwA0os6LQC4izgLAO4izmbegBPjf/vb33TLLbdo7Nix8vl8ve7n9/s1btw4fe1rX9Pzzz+flkJidPB4uq/jxiEAAABGEsPpvQLLUOoAAAAAAADZM+DE+C9+8QuNGTNG1113Xb/7rlq1SmVlZfrv//7vIRUOo0tqj/GysjJJkmVlqTAAkONmzJiR7SIAQG5y4nOM+/w9NCZmKHUASCvqtADgLuIsALiLOJt5A06Mv/LKK1q5cqUCgUC/+wYCAa1cuVIvv/zykAqH0SU1MZ4Yrp8bhwDgjtbW1mwXAQByU3ti3OmhIstQ6gCQXtRpAcBdxFkAcBdxNvMGnBg/dOiQTjrppAEfePr06Tp8+PCgCoXRKTUx3tTUJInEOAC4he9oAHBJe2I8Fus+9BFDqQNAelGnBQB3EWcBwF3E2cwbcGLcNM1kL96BiEajMs0BHx5QTx8XbhwCAABgJOlrjnGGUgcAAAAAAMieAWeuq6qqtHnz5gEfePPmzZo4ceKgCoXRKTUxXlVVJYnEOAC4ZenSpdkuAgDkpvYe48Fg9ymo6DEOAOlFnRYA3EWcBQB3EWczb8CJ8bPOOkvPPfec9u7d2+++e/fu1XPPPaezzz57KGXDKOPxdCzX1NRIkqzuI1ACANLgRBq7AQBOQHtiPByOdNvEHOMAkF7UaQHAXcRZAHAXcTbzBpwYX7VqlaLRqP7lX/5Fx44d63W/48eP69JLL1UsFtO1116blkJidEjtMR6LxSTRYxwA3BIKhbJdBADISYbiiXGnPUHeFfVbAEgf6rQA4C7iLAC4izibed6B7rh48WLdcMMNuvfee3XyySfrM5/5jN797ndr0qRJkqSDBw/q2Wef1cMPP6yjR4/qxhtv1OLFi10rOHJPamI8GAxK4sYhALilpKQk20UAgNzUnhA3Pd3bINNjHADSizotALiLOAsA7iLOZt6AE+OS9N3vflfBYFDf/va3deedd+rOO+/stN1xHHk8Hn31q1/VHXfckdaCIvelJsYTwYAbhwDgjilTpmS7CACQcxwnnvyWJJ+3h0sthznGASCdqNMCgLuIswDgLuJs5g14KHVJMgxDd911l7Zt26avfvWrOuecczRnzhzNmTNH55xzjr72ta9p27ZtuvPOO2UYhltlRo5KTYwfOXJEEolxAHDLm2++me0iAEBu6muOcdFjHADSiTotALiLOAsA7iLOZt4J9RhPmDFjBj3CkXZmD800uHEIAACAkSK1x3hvqN8CAAAAAABkxwn1GAfc5PF0LJeWlkqSLCs7ZQGAXDdt2rRsFwEAck57Z3FJks/XvQ0yc4wDQHpRpwUAdxFnAcBdxNnMIzGOYSO1x7jdfseQG4cA4I5YLJbtIgBATkr0GE9NkndsY45xAEgn6rQA4C7iLAC4izibeSTGMWykJsYbGxslkRgHALccOHAg20UAgJzjOJKheEa8p4tbeowDQHpRpwUAdxFnAcBdxNnMIzGOYYM5xgEAADDS9T3HOD3GAQAAAAAAsoXEOIaN1MT4hAkTJJEYBwC3LF68ONtFAICckzp8eiAY6LadodQBIL2o0wKAu4izAOAu4mzmkRjHsJGaGD927JgkEuMA4JZt27ZluwgAkHMcp6PHeCQS6WkP6rcAkEbUaQHAXcRZAHAXcTbzSIxj2PB4Opaj0agkybKyVBgAyHGtra3ZLgIA5Kb2buOO7XTbRI9xAEgv6rQA4C7iLAC4izibeSTGMWyk9hj3+/2S6DEOAG4pLCzMdhEAIOek9hg3TaPbdsOxqd8CQBpRpwUAdxFnAcBdxNnMIzGOYSM1MV5WViaJxDgAuGXGjBnZLgIA5DSfz9/DWnqMA0A6UacFAHcRZwHAXcTZzCMxjmEjNTFeXV0ticQ4ALhl48aN2S4CAOQcx5EMxSuw4XC423aGUgeA9KJOCwDuIs4CgLuIs5lHYhzDhtnDp9G2k9M0AgDSxHGIrQDghvhQ6n0FWIf6LQAAAAAAQJZ4s10AIMHj6VguKSlJLtt2520AgMFxHOmVV6TVq6X6+nlqaZHOO08yuk+DCwAYtHjW2+vrfqmVmH/ccYi9AJAOU6ZMyXYRACCnEWcBwF3E2cwjMY5ho6ce4xKJcQBIl82bpWeeiS+3tBh66SUpP186/fTslgsAckX/PcbjbLv3ui8AAAAwHO3cKa1bF18+7TRpzpzslgcAgMHgdgyGjdSbgw0NDcll5mEEgPRYvbpjORFnX3opS4UBgBwUT4zHK6+xaKzb9sQ26rcAkB779u3LdhEAIKcl4uzbb0u/+pW0bVv88ZvfxBPlAIChoT6beSTGMWz01WMcADB0Bw50X9famvlyAEBu673HeKI3OfVbAAAAjCQbNsQbgSY4jvTGG1krDgAAg0ZiHMNGamK8srIyucyNQwBIv9Q4CwBIj9Sh1AOBQE97SI5D/RYA0mThwoXZLgIA5LREnN28ufu2t97KcGEAIAdRn808EuMYNlIT47W1tcllbhwCQPqlxlkAQDrFE+PRaKTXPajfAkB67Nq1K9tFAICcRpwFAHcRZzOPxDiGDY+nYzkS6biRaFlZKAwA5LjUOAsASI/UHuO23fOQ6oboMQ4A6dLc3JztIgBATiPOAoC7iLOZR2Icw0Zqj3Gfz5dc5sYhAKRfapx1ep8OFwBwAuKJ8Xjl1TCNHvcxHJv6LQCkSX5+fraLAAA5jTgLAO4izmYeiXEMG6mJ8XHjxiWXuXEIAOmXGmcZmQMA0ine2sjv9/eymR7jAJAuc+fOzXYRACCnEWcBwF3E2cwjMY5hIzUxfvjw4eQyNw4BIP1S42wslsWCAEAOSR1KPRwK97gPQ6kDQPqsX78+20UAgJxGnAUAdxFnM4/EOIYNs5dPIzcOAcBd0Wi2SwAAuSGeE+9nfgp6jAMAACBHMDUbAGCkITGOYSM1MV5cXJxc5sYhAKRfapylxzgApE9ijnGv19vzdnqMA0DaTJo0KdtFAICc1l+cpV4LAENDfTbzRlxi/LbbbpNhGJ0eqWPwh0IhrVq1SmPHjlVhYaEuueQSHTlypNMx9u3bpwsvvFD5+fkqLy/XF7/4RcXICmSdx9OxbKZkyZn7FgDSLzXO0mMcANIjdSh1w+h5H8OxuYEIAGnSWyMkAEB69BdnuaUOAENDfTbzRlxiXJLmz5+vw4cPJx//+Mc/kts+//nP66mnntJjjz2mF154QYcOHdJHPvKR5HbLsnThhRcqEonolVde0c9//nM98sgjuuWWW7LxVpAitcd4fX19cpkbhwCQfqlxlgtZAEineGI8Gu0luDKUOoBsCIWkzZullDpgLti7d2+2iwAAOa2/OEuHJgAYGuqzmTcimyJ4vV5VVlZ2W9/Q0KCf/vSn+tWvfqXzzjtPkvTf//3fmjdvnl577TW9613v0l//+le99dZb+tvf/qaKigotWrRI3/jGN/TlL39Zt912m/x+f6bfDtoxxzgAZAc9xgEgPVJ7jPeGodQBZNymTdI3vhFPjkvSpz4lXXJJVosEABg5+qre0tAeADDSjMge4zt37lRVVZVOOukkXXbZZdq3b58kad26dYpGo1q5cmVy37lz52rKlCl69dVXJUmvvvqqTjnlFFVUVCT3Of/889XY2KgtW7b0+n+Gw2E1NjZ2eiC9UhPjqb8fbhwCwNB1vZBNjbNcyAJAeji2o0SP8UCglwa39BgHkEmOI333ux1JcUl65BHp8OGsFSmdTjnllGwXAQBy2imnnNJn3ZX7CQAwNNRnM2/E9RhfsWKFHnnkEc2ZM0eHDx/W7bffrrPOOkubN29WdXW1/H6/SktLO72moqJC1dXVkqTq6upOyYDE9sS23tx99926/fbbu61fu3atCgoKtHjxYm3dulVtbW0qKirS9OnTtWnTJknS1KlTZdu29u/fL0latGiR3n77bTU3N6ugoECzZ8/WG2+8IUmaNGmSPB6P3nnnHUnSqaeeqr1796qxsVHBYFDz58/XunXrJElVVVUKBoPavXu3JGnBggU6cOCA6uvr5ff7tWjRIr3++uuSpMrKShUWFurtt9+WJM2bN09HjhxRbW2tvF6vlixZotdff12O42j8+PEaM2aMduzYIUmaM2eOamtrdfToUZmmqWXLlmnt2rWyLEtjx45VeXm5tm7dKkmaNWuWGhsbk/O6r1ixQuvXr1c0GtWYMWNUVVWVbIAwY8YMtba26nD7BfmYMUtVXX1UsVhMtm1rwoQJOnLkiNavP6bCwgmKxWI6cOCAJGnx4sXatm2bWltbVVhYqBkzZmjjxo2SpClTpkhSssHEwoULtWvXLjU3Nys/P19z587V+vXrk+fb6/Umh6s45ZRTtG/fPjU0NCgYDGrBggVau3atJGnChAnKz8/Xrl27JMWH9D906JDq6urk8/m0ePFirV69OvmZKi4u1s6dO5Pnu6amRsePH5fH49HSpUu1Zs0a2bat8ePHq6ysTNu3b5ckzZ49W3V1dTp69KgMw9Dy5cu1bt06xWIxlZWVqaKiInm+Z86cqebm5uRnd/ny5dqwYYMikYhKS0s1adIkbd68WZJ00kknKRQK6dChQ5KkJUuWaMuWLQqFQiouLta0adM6fWYty0qe79NOO007duxQS0uLCgsLNXPmTG3YsEGSNHnyZJmm2ekzu2fPHjU1NSkvL0/z5s1Lnu+JEyfK7/drz549yfO9f/9+1dfXKxAI6NRTT9WaNWuSn9mCgoLk+T755JNVXV2t2trabue7vLxcJSUlyfM9d+5cHTt2TMeOHUt+ZhPne9y4cRo3bpy2bduW/Mw2NDSopqam22e2rKxMlZWVeuutt5Kf2ZaWluT5XrZsmTZt2qRwOKzS0lJNnjxZb775piRp+vTpikQiOnjwYPIzS4wYWoxYunSpNm/erFAopJKSEk2ZMiV5vqdNm0aM6CdGWJbkOMvbly3Ztq3KykrV1NRo7drjKi6uIkYQI4gRGr0xQqIekY4YMXPGcoVDITmO5MhRwB9QJBKRJPn8Ptm2rcMH92v9+oimTDmVGCFiBDFidMWIbNQjTorFVHTokNra2iRJxSUlam1p0dH77lPrZZeN+BjR0tKiUHvSnxhBjCBGUI+gHpH+GNHY2KiWlogOHKjS2LFjFQqF1NLSIsMwNHHiRK1bt1HFxSFiBDGCGDFKYwT1iKHHiPr6+uR9A2LE4GNE4u9oIAzH6Wesv2Guvr5eU6dO1fe+9z3l5eXpyiuvVDgc7rTP8uXL9e53v1vf+ta3dM011+idd97RX/7yl+T21tZWFRQU6I9//KMuuOCCHv+fcDjc6biNjY2aPHmyGhoaVFxc7M6bG2X27JF+/vP48oEDBzRp0iRJ0kc/Ks2dm8WCAUAOiESku+7qeJ4aZ//lX6QFC7JUMADIIcdqbB0/60OSpFBbSMG8YLd9Hv/w/9O//HupZs7MdOkAjEqvvCLdfXf39TNnSt//fubLk2arV6/WihUrsl0MAMhZq1ev1sKFK/TNb/a8/ZprpKqqzJYJAHIJ9dn0aGxsVElJyYBytiNyKPVUpaWlmj17tt5++21VVlYqEomovr6+0z5HjhxJzkleWVmZbPWTuj2xrTeBQEDFxcWdHkgvj6dj2evtGMyAoSYBYOgsq/Pz1DjLHOMAkCYpFVfDMHrcxXBs6rcAkCbBYPcGSACA9AkGg93uJ6RiKHUAGBrqs5k34hPjzc3N2rVrlyZMmKAlS5bI5/Pp2WefTW7fvn279u3bp9NPP12SdPrpp+vNN99MDi8gSc8884yKi4t18sknZ7z86JA6x3h5eXlymRuHADB0XS9kU+MsF7IAkB7xOcbjep1jXNRvASBdFjDsEQC4asGCBSTGAcBF1Gczb8Qlxv/P//k/euGFF7R371698sor+vCHPyyPx6OPfexjKikp0VVXXaUbb7xRzz//vNatW6crr7xSp59+ut71rndJkt773vfq5JNP1ic/+Ult3LhRf/nLX3TzzTdr1apVCgQCWX53o1tqYjwxX4TEjUMASIeusTQ1ztJjHADSIzUxHgqFe9yHHuMAkD6JuTMBAO5Yu3Ztn3VXEuMAMDTUZzPP2/8uw8uBAwf0sY99TMePH9f48eN15pln6rXXXtP48eMlSd///vdlmqYuueQShcNhnX/++XrwwQeTr/d4PHr66ad17bXX6vTTT1dBQYGuuOIK/d//+3+z9ZbQzuylmQY3DgFg6GjhDQAZ4DgD2of6LYBMsmzp2FGptU3y+6Xx46Tex7QYgSIRadu2+M2Dk0+Ov0kAQNpwPwEAkEtGXGL8N7/5TZ/bg8GgHnjgAT3wwAO97jN16lT98Y9/THfRMESpifGioqLkMjcOAWDoul7IpsZZeowDQHqk9hj3ej097mOIxDiAzDp8WGppiS9HIlKoTZo2R+o5So0sVQUF0he+IO3dG18xaZJ0551SWVlWywUAuWLChAnJ+wmztz+lWW//SYZja+fMC7R97odIjAPAEE2YMCHbRRh1RtxQ6shdqYlxn8+XXO6rVSIAYGC6JmFS4ywXsgCQHo7VEWyN3oZDosc4gAxqa4olk+IJMUuqr89KcdKu9MUXO5LiknTggPS//5u18gBArsnPz5dlSdP2/l1L1j+s4sb9Kmo6qMVv/Jem736W+wkAMET5+fnZLsKoQ2Icw4Ynpbl6bW1tcpkbhwAwdF0bGaXGWXqMA0B6GOroMR6N9BxcmWMcQCbVHOw5Fh08mOGCuCT2q1+puSX+fg4ckJqaJD35ZLaLBQA5Y9euXbJtafaOp7ttm73jaRLjADBEu3btynYRRp0RN5Q6chdzjAOAe5KJccfRmPo9ijYckDGxSo5hciELAGmSOpR6bxhKHUAmGVZuV/RCIY9qjnY8b2mVJhpSYfaKBAA5x7Kksce3d1tfVve2juf21wwAIAeRGMewkZoYLy8vTy5z4xAAhs6yJG+0Tee88H9VfnSzbNtW4zuP6bnz7lA0WpLt4gFAbnA6EuP+gL/XfZgqCECmGLHcHhrIsgu6rWtoIDEOAOkyf/58HT3a+3Ya2gPA0MyfPz/bRRh1GEodw0ZqYrypqSm5TGIcAIbOtqU5O55S+dHNkqRYLKbShr1asOVRLmQBIE1S5xiP9RJc6TEOIJNyPTFeV9e9pVFzSw87AgAG5dChQ3026uR+QgbV10u7d3OzHMgxhw4dynYRRh0S4xg2UhPjbW1tyWW+6wFg6CxLOnXT/0s+t9uTN7N3PMUc4wCQJqlDqdtWL5VYh8Q4gAzK8YxFr7EWAJAWdXV1fdZdc/xrZnhwHOm//kv65Celz31Ouvpq6fDhbJcKQJrU1dVluwijDolxDBseT+pyxxOGmgSAoesaSw2jY5kLWQBIk5S7hqlxNpXh2CTGAWROjreA7C3WAgDSw+fz0WM82954Q/r97zue19RIDzyQvfIASCufz5ftIow6JMYxbKT2GJ8wYUJymRuHADB0XS9kA8FgcjnH75cCQMakTDHeKc6mYih1AJnkRHK7otdbrAUApMfixYtJjGfbY491X7dxIzfNgRyxePHibBdh1CExjmEjNTF+4MCB5DLf8QAwdF1jaagtlFzmQhYA0iQl2KbG2U4YSh1ABtnR3K7o9RVrAQBDt3r16j4T44z0mQGbN/e8nl4OQE5YvXp1tosw6pAYx7DR2xBo3DgEgKHr62KVaykASJMBJGLoMQ4gk5xwzxU9w86BhHlfMZcKLgCkjW31Hm9jESq2WROJZLsEADAikRjHsGEYHb3GCwsLk+u5cQgAQ9c1Me7xepLL9BgHgPRw7I6bhqlxNhVzjAPIpN6GUjetHKgAxmK9xtpcH0IeADKloqJCVqT3lvZ2Lw2wkAEkxoGcUFFRke0ijDokxjGsJBLjgUAguY4heQBg6LomYcyU+StiYQItAKRFSu/F1DjbdR8S4wAypbeh1E1r5CcynHCk11hrtZEsAIB0KC4u7jP5bYeIt1lDYhzICcXFxdkuwqhDYhzDiqe9sffx48eT67hxCABD17WRUTSlFw0tvAEgTVIqrtFeeivSYxxARvXaY3zk1//scLTXWBtrG/nvDwCGg507d8oJ956A5X5CFjFtCJATdu7cme0ijDokxjGs9NTYmxuHADB0fY2+YUQjxFoASIPUodT7QswFkClObz3Gc2CO8b6S31aIZAEApEtf01PYkZH/fTJi0WMcAAaFxDiGlURifPz48cl13DgEgKHrOieY3+9PLnusCPOMA0A6pAylnhpnU9FjHEAmOb30JjNyoMe41RbpNdaSGAeA9Jg3b16fifG+epPDZSTGgZwwb968bBdh1CExjmElkRhvaWlJruPGIQAMXdcLWSulC7lpRRmBCwDSILXHuNXLUB2GmGMcQAb1Nq1DDrSKjLVFe421DKUOAOlRU1PT53DpfSXN4TIS40BOqKmpyXYRRh0S4xhWEonx1tbW5DpuHALA0PWVGKfHOACkiTOAxDg9xgFkUi+VPCMW7RSzRiIr1HtinDlvASA9jh8/To/x4YoeDkBOOH78eLaLMOp4s10AIFUiMW6mTDbe17y4AICB6XaxanQseqwI11MAkA6pGW9jYLsBgJt6G0rdcRS/2PaO3NtCVluk11hrh0jUAEA6eDweRfvomUyP8cyIxaSGxvjPwgKpoED0GAdyhMfjyXYRRh16jGNYScSAqqqq5DpuHALA0HVNjAeDweSyx47SYxwA0iB1KPXUOJuKHuMAMqqvxPgIrwDa4WivsZY5xgEgPZYuXdp3j3ES466zLGn/funYMam+XjpwUGpoEIlxIEcsXbo020UYdUiMY1hJdBQ/ePBgch03DgFg6LperIbaQslleowDQJqkDEucGme77kP9FkDGRHtOfjuORvwQrFYo2mustRhKHQDSYs2aNSTGs6ypWep6mmtrGcYeyBVr1qzJdhFGHRLjGFYSiXEn5aYiNw4BYOj6umAyLXqMA0A6pPYY740hEuMAMseI9ZywsJ2Rn8zoK/lt02McANLCtu2+vy+i0dS2oXBBzZHu6yJRpg0BcoXNDYKMIzGOYSWRGC8oKEiuIy4AwNB1vZBNnb/GY0VIjANAOqRUXHudJ4we4wAyqY9e4XZkZFcA7bZIr7HWpsc4AKTF+PHj+/wu4X6CyxxHvbU7iLXxXQfkgvHjx2e7CKMOiXEMK4nEeF5eXnIdNw4BYOiMaOeWxF0T4yN8JE0AGB5Susv0lqxhjnEAGdVHtmKk31C3w1ES4wDgsrKysj7nsjbtGIlxN/Vxs8Zqo8c4kAvKysqyXYRRh8Q4hpVEYvzYsWPJdZaVpcIAQA7pOpR6JOXC1mMzlDoApEPqUOqRXm4gMpQ6gEzqbSh1KT5H90hmh6O9xlrmXQWA9Ni+fXufQ6nTY9xlfTRKGOnf4wDitm/fnu0ijDokxjGs9NTYmxuHADB0Tj9Dn9FjHADSYCATLDKUOoAM6isxPtJ7Vdt9JL9H+nsDgGGlr/sJNLR3V1+J8dZwBgsCALmDxDiGlUSP8bFjxybXceMQANKgy41Dn9+XXDYtLmQBIC1SKq6pcTYVQ6kDyBjHkaw+hlIPjewKoBOJ9hpr7ejIfm8AMFzMnj2bOcaziR7jQM6bPXt2tosw6pAYx7CSSIyHQqHkOm4cAsDQde0tZFsdwZUe4wCQHqlDqafG2a6o3wLICMvqFJe6bR7hN9SdUKTXWOuEGEodANKhrq6uz8S4aUWZBtNNfZx7m+86ICfU1dVluwijDolxDCuJxHhLS0tyHTcOASANurQytlKuXGnhDQBpkjKUutXLHUJ6jAPImFhM6mOGh5E+3LgTifYaa/uaDxcAMHBHjx6VEe09ActQ6i7rq8d4G4lxIBccPXo020UYdUiMY1hJJMYNw0iuo9UhAKRBHxdTHjtKj3EASIcBzDFuiDnGAWRINKo+OozLCo/sTEZfyW8S4wCQHoZh9NtjnMS4i/q4lzPSG7gBiEvNhSEzSIxjWEkkxidOnJhcx41DAEiDLheywbxgctljRfq61gIADFRKxTU1zqaixziATHEi0T7b61gj/Ia6E470GmsdKrcAkBbLly+XYn3MMU6PcXf18X3mhPmuA3LB8uXLs12EUYfEOIYVjyf+89ChQ8l13DgEgKHrOvRZKBRKLpMYB4D0SJ3LNzXOdkX9FkAm9Ncj3B7pc4xHor3GWidKlgYA0mHdunUy+ugxztRs7uor+c0c40BuWLduXbaLMOqQGMewkugxbqfcLeTGIQAMXbc5wVJ6D5lWVOFwZssDADkptWtmL7006TEOIFOsfhLfIz157ESivc+hzlDqAJAWsViszznGGUrdXX19l9NjHMgNMYJoxpEYx7CSSIzn5eUl13HjEADSoEsLb9PTUQXwWBH10bERADBQKYnx1DjbdR/qtwAyob/E+IifmzQa7T3WMhwSAKRFWVlZn3OMM5S6u2ItvfdicGgEBuSEsrKybBdh1CExjmElkRgvLCxMruPGIQAMXdcW3l6PN7nssekxDgDp4FgdFdfUOJuKHuMAMqXfodRHemI8Euk11vaVxAEADFxFRYUMq/eYSo9xd8Xa6DEO5LqKiopsF2HUITGOYSWRGD969GhynWVlqTAAkEOMWOeLqUhKLxqGUgeANEnpMR7po7ciiXEAmdBvj/HIyM5kONFo77GWxDgApMXWrVuZYzyLrLY+kt+MjgLkhK1bt2a7CKMOiXEMKx5P93WO03m6RgDAIMT6vpAlMQ4AaTCASis9xgFkymgYSn1Q2wAAJ6RrQ/tUDKXurj6/y0mMA8CgkBjHsJLoMd51XgVuHgLA0JhdhlL3+X3JZYZSB4D0cOyOxHhqnE1liDnGAWRGfz3CR/rcpEYk0musJTEOAOkxc+ZMGbHeE7AMpe4uq7WPOcb5rgNywsyZM7NdhFGHxDiGlURivOtwaNw8BIChMWNd42pHYE0MfcbUFQAwRCk9xu3eKrAOiXEAmZHrQ6krGu091pIsAIC0aG5ultlPj3Ea2runr+9ygx7jQE5obm7OdhFGHRLjGFYSifGuwYCbhwAwNF2HPrNiHVlwjxW/mOJiFgCGKKXSmhpnUzGUOoBM6Xeo9JGePI5Fe4+1ffRuBAAM3OHD1TKt3r8vTCuq1tYMFmiU6XOO8WiU+UeBHFBdXZ3tIow6JMYxrJi9fCK5eQgAg+c43XuMpzLt+EUuiXEAGJrUodR7w1DqADIl54dSj/ZevzVGetIfAIYJy1KfiXGPHVFbWwYLNMrYod6/62xHI7+RGwBkAYlxDCuJxPjEiRM7rWd4XwAYvJ4uZIPBYHKZHuMAkB6GOhLjqXG2E8eR49C5A4D7+usx7kRH9lDqRjTaa6ztOloSAGBwlixZLo/dR2KcHuOucsK9J8YdWxLDqQMj3vLly7NdhFGHxDiGFY8n/rPr8BH0qgGAwbPtjuR3QjgcSi6TGAeA9EjtMZ4aZ1MlkufUbwG4zQrneI/xWKT3WEtiHADS4o03Nna7n5DKtEmMu6nPHuMkxoGcsGHDhmwXYdQhMY5hJdFj3OrSRZwbhwAweJYVH94sVWpPRY/FUOoAkBYpldbeeoQbjt11VwBwRX+Jb2eED79qxGK9x1oS4wCQFqFQNDn9Wk/oMe6uvkZ/sZ2R38gNgBShgUvGkRjHsJJIjHcdDo0bhwAweJbVkfxOMD0dVQDDsWTYFolxABiqlAxNapztaR/qtwDcZvd3s3wkD6Vu2zKsWO+x1rbkWARaABiqoqLSbvcTUpl2VKE2h2kwXdLXUOqSFGsloQaMdKWlpdkuwqhDYhzDSiIxXlxc3Gk9Nw4BYPDiifHOF0ter7fTc48dJTEOAEOUOpR61zibwFDqADLFifQzlPoI7jFuh6Ny1HuslSQrNHLfHwAMFxXlE2Q4fWe9TTumtrYMFWiU6S8xHm3luw4Y6SZNmpTtIow6JMYxrCQS4zU1NZ3W0+oQAAbPttVt6LNIl4sr0yIxDgBD15EY7xpnO3YhMQ4gM/oaflWSFBu5PcYTSe9eY61IjANAOmzdtLnffTxWhOHUXeL0M8RyrIUbOcBIt3lz/3EW6UViHMOK2dsoaNw4BIBBs6K2TLvvG58eK0JiHACGagDD9jLHOIBM6bdH+AjuMT6QoWNjbSP3/QHAcOH018hK8R7jJMbd0d8c4vQYB4ATR2Icw4rHE/85ZsyYTuu5cQgAg9dTbxmfz9fpOYlxAEiDlDnGu8bZrqjfAnBbf0OpGyM4MW6F4++tr1hLj3EAGLpJlVX97uOxoyTG3dJfj3HmGAdGvJNOOinbRRh1SIxjWEn0GI91GdKNG4cAMHg9zUnlOJ0DK3OMA0AapCTGu8bZBHqMA8iU/nqZOSN4KPVEIqC3WCuRGAeAdAg1tvS7D0Opu6ifOcatNhLjwEgXCoWyXYRRh8Q4hpVEYrypqanTem4cAsDg9XRTMBazOj2nxzgADJ1jdyTGu8bZBEPMMQ4gM/pLjI/oHuPt9dveYq3EUOoAkA5HDx3udx/Tose4W4xoPz3G+a4DRrxDhw5luwijDolxDCvMMQ4A6WeH+m9BbFr0GAeAoTL66LnYdR/qtwBc10+PcCM2cm+mD6SH3EDqwACAfkR7b4CUQI9xF/XTiI0e4wBw4kiMY1hJJMarqjrPX2P1XwcDAPTCDne/kAoGA52ee6yIGLkHAIYmtcd41zjbsRM9xgFkRn89xvtLnA9nifptr7FWDKUOAOkwa+q0fvcx7RiJcbf002OcxDgw8i1ZsiTbRRh1SIxjWEkkxmtqajqtH8HX6wCQdT3NMR7uso6h1AEgDVLmGO8aZxMYSh1AxvTTy8ywRm7iOJH07i3Wpu4DABi8vTt39bsPPcZd4jj9ju5CYhwY+bZs2ZLtIow6JMYxrOTlSRMPrFaw6Uin9bt3Z6lAAJADekqMOynJG0ny2AylDgBDlhJbu8bZrvuQGAfQm9pa6ZVXpI0bpba2wR/HifY3lPrIbYFutQ+T3musVc+jJgEATky0tf8vIo/NHOOuiEb7vWbguw4Y+UIM4Zlx3mwXAEhVse0FnfvKdzXPKNXLE7+vtvyxkqS33pLe977e5yAHAPSupwsls0tApcc4siUclo4ckcaNk/Lzs10aYIhS7lx1jbMJzDEOoC/790uPPNIxnVhFhXTFFYP8juynl5lpxW+4j8TrbLu9N3hvsVYiWQAA6ZDn9fe7j8eKqInEeNo54Yj6aP8lSbJD9BgHRrri4uJsF2HUITGO4ePZZ+X5wQ9UVODIqj+u9zz3Nf3tPXcrlDdGzc3xGwRTp2a7kAAw8vTUY9zn61wFMK2oolGN2JujI92xo462PLlLsaN1qjzvZM1fXpDtIrkuFpNefDHeIy4Wkzwe6aKLpEWLsl0yYPAc25HRvtw1ziYwlDowTDhO/JGmio9tSzU1UkGBVFQ0+GM8/nhHUlyKNx575hnpQx8axAH7G0rdsRSLOvIHjD73601NTfw6vb4+fhrnz5fKywd1qBOWSHr3FmtT9wEADN7Y4pJ+98nWHOOOIxmD+wpzVW1t/Lv70CFp8uR4Z6/CwhM/TrSl/6Q3iXFg5Js2bVq2izDqkBjH8PCXv0gPPCA5joqKpCNHIipqOqgPP3G5GosmqbpykZ57Z54mTvNJHlOGYUiGIcNjyjANOYYpGYYcGZJpyjFMOTJkG6ZkO/HWdU78Z/yGpSMnsV7xdbbpVTivVNGiMnnyA/KZlgJGRE4gKMM0ZEQj8tjReI3LNOMVr0Q5TKPbsmFIBYWGpk2TAgHp2DHp6FGptTl+F9QwjeTPRCXO75dKS+MVu6am+I2RxLb2w6u0VKqqkvbtk2prYgq32YpGHEUjjgoLHFWNi6gkclR+IyqnbKxUXBx/rcfs9NOWqeO1ho4fl5pbDNm2NHOmNGPG8KxUjkQkGDFc9DbHeDAvmHzusSLt6+PTWqA7x3a046Hn1PbKGzKrKnXS5y5S4cT+bxL0p/5oVNs++S1V7lktSQr/qlhv3PoNnXbJSUM+9nBQWys9+2z8xnkgIJWVxb/nDhyQWlvjN1EK2mrVmjdWTzzh0VtvSRMmxBMLPl/H95/UsWya3X9K8e/apqZ4r7rKyo7jAJnTeY7x1DjbsUt8n5aWTJUJQCeOI/3P/0h/+lO8wv7P/yxdeeWgK+6WJW15arfq/ut/VfbOGzoQKFb40k/o9C+eecKH3LFDqjtmae6OpzTlnZcU9eVrz/TztNE5W+96l0cVFSdYuH6GUpekWFtU/kD/vQFTOY70lz872vK3w6o8/IbKj26RYUX0p0mnK//Cc7XwNI8MQ/J64w3fPJ74sml2fiS+wxMPOxJT64FaWS0h+fO9ChTEH56AN34Arzd+wltbZdZUS+oj1opkAQCkw4HdezS9n31MK6pIJN7g2ZuBbENzk6PX7npO9sY3paoqzfvSRZo8e3A3MqLR+PXqrl3xhm2zZkmnnDK4RLYkhULxakZtrWTYljbXmzp2zNDVV8e/D0+obCTGgVFh06ZNWrFiRbaLMaqM+sT4Aw88oG9/+9uqrq7WwoULdf/992v58uXZLtboEotJf/xj8iZhfr5kmh03FYubDqi46YBm73xaeiVDRfLmyRsLSXIU8+bJ8vgUCDee8HEi/iK9XTpdMV+e8luOakzzYY2P9TY3j6GYN6CW/HJ57KjGtNXKcGw5KVlqxzDVFhyjtw1T/kizJoQbejxSS/tjIMa2PyxPQIfLZmnvnFNVPmeMfGOLFTbzVL+vUdHGNnl9hrw+Qz6/oYIiU6Vj4o0AYpahmG3Gf1qGLNuQYjF5GmrlO/SOfIf3yaeo8gpMmT5TjsxkQwZ5PJJpyrKltrCptjZDbWFDjhNv9GCa8US+TEN2c5ucujqZhuQpzJPy8mT7Ah3JftOQDCUbTciQZPTQgEHq1LDBkeLHjkTlKS2Srzgvec4NqXNGJvFckszU1gMdx7ZsQ237jkrbtsqIxeQsXKRJH1wcT551yeJEQ5aOrtunyKGj8hYEVTixWCWTSxSsKJFKSuItJTyeeG08EJATCquuTjp81KvGFo9CYUM+X3y3QKDjXlHiBlRvz1NvTGF0cCL995YhMd43x3b05n/+SIHn/6TE6dn10kua+ui3VTql/yGPLCveOMpx4n+vJSUdF8U77nhU49uT4pIUCDeq9e671HD2gyoZ3/eN6lhMOnhQam5OfH/GQ0zqsOSWJTU2xpPMPV7cO078QJYVvytgWfHnqesSLX3a47ZsW44j1cTK1Gb5VWVWyx9rTb6m7mhMu3ZYqj4QU81hS4Ztaawdk+lYsu2YDNvSjHCjShr3a8LhdfLGQor68nWwarkaN0/SMUnHHVtG6kNO5+ftDzmOTMdKLhuOrVbH1m5Jb3t8Mvw+2V5f/PtBPSTXDUdG/Kum42diu8dUYPZUzfrX03R47UE17Tgs++hxGVYsud00JdMT/x4wTENG+7JppqxLfXg9CozJV95JE1R8+nzVbz+ixjXb49+dPlMev0ceryHTa8a/31Ieps8T/+k1JY8ps33ZE/TJO6Yo3nrO61UoJNXVxX9die+J1OREvw3gbFt67TVp8+Z4y4KpU6U5c6Tx4/v9rPepqUmqrpYmTcrdQGP3M9ahpJO3/k5teWVa882gdk7IU4sVlDffr6KxflVO8Wv6bJ88QZ8cn1/ePJ+8+X55g16Znu6/uGg0/vfd2hr/tfl88d+1z9fxSPzuAbR79FHpt7/teP7EE/Ggf+WVJ3yo5uNhbbr2Rxq78VlVtq/zR5pU9LNvaaM/pkU3nBv/So06sqOW7EhMoRZLx4/EVHvUUu3R+DqPLHmcmHZuDuv9q3+g4sb9yf+j8sgGnbL513pl4xmatLhcXo8dnxu8/WFYHcseWQp64t/lTjQmo/pwv+/h2U8+ohkzDXmc+Pe0rPj3tmHFpPafhtX+045vP7QvpvJQg6a31nQ61sSDq6XV96quZFrH97Yd/45OfFcbyZ+2TLv7up6YKd/bav+etq0ed+2EHuMAMHRGrP9GVon7CW1tgx81ZaAsS1p/zUOavP6P8RW7pOrNL8n3q2+p8qQTm3fEcaTf/U9Iwd8/qiXVb0iOo1DeGP118kLN+9JFmnfKiadO1qyRPNu26AOv36+ipoOqLZulN077tNatW6ATTTlEW/v/HhtI8hwY9ZqapBdflCNDxjlnD77lC3KG4Tj9zVSRux599FFdfvnleuihh7RixQrde++9euyxx7R9+3aV9zP+V2Njo0pKStTQ0MAcAOnQ2CjddJP0zjuSpP37o2pt82W5UEB2mF3y8Ik8VCwmxVJuANmmt4eHr5f18YeVst3xeJN3zw1ffNnwxR+JdaYvnlUx/V4Zfq9MX/tPv0++PK98ed54b45Cn/ILTZktTbIbm2U5hhzDI4/fI2+g/ae/PeHj9yQTPqa3/eGJN4ZwjI4RHxzDlK2OdbZjJEd9sGO2HMtOLstx5MQs2VZ8NAjZdnwf24nv175vcr0Vf40ds+MJvpT95DiyLSd+Q7H9NYmfXZflxF/XbR/Hkazu+ySf2078bp7dPoRn6rb2ES7iy0581AupY99u+3Qsdzxvv6nYPkKGUVOtgn1bO33OrFhMni5NuVvzx6m0RAq2d7pJDAjsJAb+ddTecCQxEHCX9Y5kOUa8pXhUsmXINCR/wJDH097+yUh5bSI756T8f8nt8bfRFjIUjaV2F05ptNLpZ/s/nX523s8xTDmmR2r/6cQzmh3dlBJ/bO3ZQ4/fVCDPI3/QlO/5v8rpYWy45sIJarj8epUGQ7IamhWrb1Yg2qzyghYFjbDs1pBqD4V07EBIRiQsTywsbywknxVS0AzLbG3pNY9WP/00WWMr4g0b2m9wy7LkN2MqCMTkWJaa66KyI5ZMOxZ/OJbkOGotGC+naqJ8XkexQzXKb6yWx4rI74mpwB9Tni8mj2HJY8dkyu6xF3aiDZPR/hGLxuK/E0dSOBS/rki0uTAMKdDelicakyJcm2ecaUoer6nmwgqF7IAMO/65kON0jKyTiLFme4My09OezI//DcRHlXFUemyXfFZIHq9kJb57HClUMDY+ko7XlOH1yDBNyeuJH8fb8feTWDY8ieemAvU1yju4U7IdhW2fjk1bokjFlHhS3x8/XrLVlsfT/hk0Oo+wY6aMzNM+4k7iuWlK8vnk+AOyvX45Pr9stbcAM1IbLpidjm20Z1tMUz03ZDCNeELa6Fjua7+9P3xa3t/+UlLPcXYobI9fjtcXf/j8sk2vYqGYTCcmjxWVx4rItGOyTY9s0yfL45fV/tP2+tvPj1/y+eX4fJKvfZ3PH2894fPJ09Ys34E9Mtualcw+me2x0+OR4/XG42Z7Kzsn0drO45GR2vrO44nXJzymDK83+fs1vPH1iZ/yttc1kp+X+MhQiUYkMoz45y0xUlSiMaTZsdxpfXuDkeTv1dt9H8MTr3fI4+n4nSYan3RdNo1OI005dryOkFjuOipV8pEyKlXqfsl9u+yf2Lfr9tRjd1ru6dhSp3Im6xvquayJ/6i9+pF8b/EXOEod/cBIqWeYphTwJ2sGncqSWDbkxL9+Haf9e6Rj/+TfXXsrneTvs8vvNTE6WHK0rtTfd0pDo+TfXw+/68Tvs9Pvdcc2eb7+VUnx76rGhnjDEo/XUOu/XanScr8MKyY7FJETjshqi6i12VK4taO+GK/32bIbmlWwqf+W245h9prwxdD1FWtDl/27Zv+fD0rqXnUcqB7372HliRx3oPsO9/1GC8d2FDnWqGhDq1RUJLOoQKbH6DbqQabO22Du4mb9NX0crNOmnvbrYV3qd99AC9Xv/zPQ/yPd72UAjSq7vn6g76W3/ZKLXV7b26Heuu9xjXnyf/st3rFx82RaURXm2wP79XT5o0nea0i0gmrfJ3mfoL2Fc8GezT0eLurLV95NNyavHzxeQ2bAJzMvIDPolyfgjTdE9kgeX/yaYNPqNuV9/07ltx7rdrya8QsU++rXNXG6P37NLEceo/2eUsySE4s3FrNjdqefb/3yDc1/7b+6vlntmX6eqi5aIsO2ZEXaXx+LdTpWspG6FV9n1NWqcO3zfZ7GwxOWaOrXPqGCgo5RSRP1sI6T2+UXkvgsxSwZba0yQm0yImE5wTzZpWXx+4Wpv6P2n4l7Op22qePeUXI/9b4tsZzsyNTpNKV0auraSckw5OTlxxtYx2JSNCrT6rnhQLd4nPj/ethhQN/zKY3se/3/umxMvo8BHCO5rr9j9LF/j+ezp1X9nYuuZejl/PR5LgawLh379rU+Vejldaq76R6FjrcqEpWsQL52vP/zGrdykaZNN1RcYsjjTXRw6Phu73qNk1zucr3V9dovuZzc0MP+KddlRRMK1RCuV2VlZdei4wSdSM52VCfGV6xYoWXLlumHP/yhJMm2bU2ePFnXX3+9vvKVr/T5WhLjLqivl776VenAAdXXh3Skpufh0AAAQxeLxuTtY05GAMDQpMbZsjFSbV2WCwQAOSh367Q5lp3u9nZG0Pvr0lhoRJXddaP2lvKokrtxFoA7+v6edAaSzU6TkdA41fP5zynvonmaOHFitosy4p1IznbUDqQbiUS0bt06rVy5MrnONE2tXLlSr776ahZLNoqVlkp33ilNmCDHaVNlRZfRqgEAaRMbwHBoAIDBS42zY8Z0jMYBAEif3K3TOrn1cLo+7JHz6Jb8HQbnc9g8MBrkbpwF4I6+vzt6miLPrceI4Dg6cOBAtksx6oza5l7Hjh2TZVmqqKjotL6iokLbtm3rtn84HFY4HE4+b2w88fmmMQBlZdJ3v6v6b3xD06NRFURs1e08qlBNkxL1sJRRKOLXV10O0VNDZKOX5W6LRsc1WmKEwdT/wOm2MDwuBcz2UYbsERLvAQCjl7d9rmPTEx8FLS8o1dZ2DMsO5CLDkCorpH37qa8ByK6CfKml++wwQ1JYEJ9X1iK+AcCoMX68dPRotksBACPb6B3PO7tGbWL8RN199926/fbbu61fu3atCgoKtHjxYm3dulVtbW0qKirS9OnTtWnTJknS1KlTZdu29u/fL0latGiR3n77bTU3N6ugoECzZ8/WG2+8IUmaNGmSPB6P3mmfa/vUU0/V3r171djYqGAwqPnz52vdunWSpKqqKgWDQe3evVuStGDBAh04cED19fXy+/1atGiRXn/9dUlSZWWlCgsL9fbbb0uS5s2bpyNHjqi2tlZer1dLlizR66+/LsdxNH78eI0ZM0Y7duyQJM2ZM0e1tbU6evSoTNPUsmXLtHbtWlmWpbFjx6q8vFxbt8bnrp01a5YaGxt15MgRSfHh6tevX69oNKoxY8aoqqpKW7ZskSTNmDFDra2tOnz4sCRp6dKl2rx5s0KhkAqvuEKt06frzTfflBxHJ/n9shsbdeTwYclxNG/OHO3ds0ehtjblB4OaNHGidm7fLjmOKsvLJdvWkepqGY6jGTNn6tChQ2ptbVUwL09Tpk3T9u3bJcPQ+PJyeXw+HT50SGY0qhljxqj27bcVqamRv7BQVdOn6+D69TJiMeVPmybf2LE6Ul0d/11VVamurk6tLS3ymKamTZ2qXW/vkuM4Kikqkk9eVb+0Ub7D1corKlZkTLHCZQXyVpZo+kknac/u3XJsW4VFRSrIy1d1dbWculaVRKSQbSlU5Jfh92nqlCnat3+/bMtSoM2Wf+d+tR45JqcoXyXvXiGjvECNTY1yJE2qmqltOw6ryVcoX0GpxsvSkf37JcdR2Zgxikaiam5qlGM7mlBZqYb6GgX8IRXm+xTb1aLWtRvkRG35fF45Tker0MLCgMKRsCzLkWTK5/UnG4p4fV4ZkqLR+L6BgF/RaEy2bcswTYXHTlON5ZfhOAr6vTIlRUKtMhxbBXl5ioRDsqyYPJIKC4KKRprkMW35vD6Zhqm21lYZjhQsKlY4P6hQ1JLCMY3xe9VSf1xyJJ/HK9M0FQ6FJcdRMBhQLBpTLBaV6TjKy89XW0uL5DjyeLzyekyFQyEZcuT3BxT1eBW1bflCrSoMeBRua5MjRx7TI6/Ho0j7ZLk+v0+2bctqn+g7GAwqHA7LcRyZHlM+r1fhcESOIXkCeTJj0eQ5DAQDikQicmxHpmnK54vvKyl+vhUfnip+DgOKRiOybUemacjn98ffmySv1yvD6OV8G4YCAb9CyX09MgxT0Wg82+QP+BWLxWRb8fmEA4GgQqGQJMnj9cg0TUXbM1N+v18xK76vjPh7DbW17+vxyPR03teyLFlW+3nJaz+uE9/X09c5zAsqHArJcSTTY8rr9SoS7tjXsW3Fkuc7ED+/Xc538hymfGY7n29DPl/nz6x6Od+Gacjf3/lOnsMeznfqOex6vlPPYQ/nO3kOezjf3c6hZfd4vk2PKa/H289nNn6+i0sclRRb2n8gGv878vnkOL2c754+s13OdzSS8pn1DTRG9HS+jeRntvv5DgzoM9vtfA/kM3sC57vTZ7bf8909RsTPoU+O44yKGJGf75PXF1MwEFZenq3S0lLV19fLcRz58vLkr6xUfuCYVBtT0PDK443JNEKybUMlJSVqbGyUbTvy+rwKBIJqbo7PfRwsKJBlOwpFonJkqHhMmVramuXxWnKMgAyzQHU19VLUUp7XlOM48d+VI+XlFyjUFpJl2zINU/5AQG2tbXIcQz6fT7YjtbX0HiOikfhnwOP1df9850iM6BaTiREnHiNMU7Zly7bDamwMqaCgQOPH2Tp61FIkaigvmKdwOCzTtCTDK8f2jcoYQT2CGJErMSJcMkae4iLlH6kecD3C5/UqFgu3nxe/bNtWzIrJ43E0ds4UNS6dpeqXq1Wya6f8XrPXGBFs/8xajiPHG5DXH1RrJCbL9MkXDMo2PWooKVPRyWWaEd2jmo17VF9nyLY98vnin0PbMOXxB2SbPoVjlizTq0B+gUJRW1FHkulVoLBITS1tsk2vPMF82eWV8i0dq+J/mqRZzREd+v2Lso4fkz/o0bjych2uOSZbHgXzC+TxB1XX0CTbMFVaNl4tbSG1hiOSx6vx5RN06EiNbMOj/Kpy5ZWP1+EDR2RFbI0pGqPmuma1NDbLtG2NH1+h/bVNshxD+R5TQY9H9ZGw5r76BDGCGDGsY8RAzzfXGtQjhluM8Pl8yeeJz6xpRmUYEU2oLNXed0JybGJEpmJENBKS4nctiRHEiGERI6hHDC1GtB47phnTF2r16tWSpLlz5+rYsWM6duxYMhe3Zs0a2batcePGady4ccmOvbNmzVJDQ4Nqamokdc7FlZWVqbKyUm+99ZakeC6upaVF1e25rWXLlmnTpk0Kh8MqLS3V5MmT47k4SdOnT1ckEtHBgwclacTkPjdu3KiBGrVzjEciEeXn5+t3v/udLr744uT6K664QvX19fr973/faf+eeoxPnjyZOcZdsnnzZi1YsCDbxRieHEdqbpYKC+Pdj9J56GhMsdpGhY82KnK8SXZLmwonFCk4rjD5f1sxR8ePOWppsmUajnweW16PI5/XkdfjyDQcmR5DRmmJvOVl8uQHVF8fn0LeMOIP0+x4K44TX1dcHH+YGZrgITnygNPxf0ajUsqfebcWW47tdNqYfN6+Y+K512+qoNijhmNR7X9xj8LHm+PDtziOZNsyFP8p21agolRjl0xTLGypbm+DGvc3SA0N8rY2SpYlJxJVrLZRikZl5geVX2BoXGlMpYUxBcyorHAs+bAjMTnRjp9ONConEpMTi0nR+DpFo1KsY52sWPy53fH7SH3YXdcl9nPjlzLK9TiyRWJD4m898UeU2KnH5fijY84eo9Pr7fwCmcuWyjtLOqm+Ts2Hm7T3oE81tV7ZPcywYiSGPkwsp6w3jI51iWXDkPw+R8Fg/G8rFnUUDkuO48hI/FEZii+n/JSU3J56TL/PUTBPMuXISfnwxUf06ChbsmS2I0NOx99v6h+7Y8uwbcm2ZNhWclm2LcOykn+XiXV21JIdsZT6px/NL9FYT4MkKRSSLKv33lGWxy/LE1DMG5TlC6hoXFCF44KKeQJqjgUVUkCh+rDyDuxUftsx1Y2dJd/4UpXU7JSnoVbyemR4PJLPJ3k8ktcrx+NVOOZRyPLKNr0yfV4VlnhUNMYrb9Ar2/Ao1BxVdOc7Mpsa5Hi9UkGBVDVRkfwSNbV61dDiVdT2yDa9nR6O2WWd4ZHt8ck2PHIMMzkUlelYcgxTPtNSSahaCkcUKq1UvWes8oq88ud55A16VVHl0fyFXhUUx8ueeA/yeOKPYFAqKen4fDqO1NDQ8UVhmp2XU9dlYE6qcFj6w48PyPn7CypoqVFjyWRNfN8pOv0jE2Tmx8fEduz4d0Es6si2ujxsybHseCy1HNkxW7YtRVujMv/wlLx/eVrRkCXTkPzjixX9t08qMmWmYhFbVtSOvzb1EbPiP22n2za7NaTosQbF9h2Sr/aI/H6pZKxHgQKvbNuQbXW8xk4ew4l/zySOZ3d+3pBfqXcmnK7mSXM1qXWHxlZvka+5Vk6s/W/DsuMffiv+9+RYtgzbiv9R2Hb735glQx3DmEV9BfLGQvLaEeVZTXLs3O5dGAmH5Q8GFfjsZzTtrMlSJCJNnRr/HIdC8UdbW/wRiaipLqpDe6MKNUbkU1QeKyInHIl/r4cjUiQiJxKVE47IiUZlRKPy5XuVV+STv8Anw+9TzPDKjtpyIlHZofjrnUi047WRiIxIRIpGZUQjMmIdPxPhMlpWrlj5xPjfWXvdJR7sLCkWi//OrVhyXWLZsGLx+GrFZCQ+Fw71BgxfMW+eJEfeWGgAexsyTEOOaUpG/Lsosez4A7IXnaYZ37hSgfHF2vXTv6tt3VuSZcn0eeQJeGV4PZLPK8Pjken3Kr8o/t1dUOyR6evhe7KkRJo7VwoEJEl1h9rUvLtGhcWmCku9Mv3xYxp+nwxfx2stx1RbW/zP1+eLH67H66uWlvhPb/v3v+FRzOr/uzX16zfxf2Rb4t5B+LmXdezL96i5MYe/WAAgCyLhsPzt30dSPP5PrIpfZkpSq7dIh1pK1WYHZHv98evY1PsaA0g9dNx3aL8XkBxiKb7eaK9UOq1t8h49rNTapc8Xv9U1ZFVVmnTrv6v6lgcUO3J8yIczTclTMU5NBRMU2LNNhtV3Ibve+kneGzIMOR6PbH9Q5pTJmubZJ7upRdXV8csIAAPn80penxRqGz7XqMZnr1fknCpyYWlwInOMj9rEuBRvQbF8+XLdf//9kiTbtjVlyhRdd911+spXvtLna0/kJOPErV69WitWrMh2MYDRwXE6bna3J85lWR3LqY/2dXYkpmhbTJHWmCItUYVb4stOXr5UXCyP15AsS1Y0nkCJRdoTjNF48sSOWt2TO7Yjsz2BkvhpqGOd4cRb68kwZHjiCTLDjCfN4s8NGYllI37z0vB6ui97Ol5nes2OdX2s73QM05CRui3l/zXMjrJ1Om7KtuRyl/Il71omkn6drojSr2ucjcWkurr4/ddAoHMxuj5Gm0jYUaTNkuHYyiv2yfQY0t690q5dkmmqORZUbVuevMX5Gn9SkVqMQtWF8xWxPDLNeDumsWMlv7/7sR1HOnZMqq9zNKHKUGGhOm4cDPVkJ1oe9bA6HO74k+7pEYl05MHaG/qquFjKz4/nyAoL40Pnpd5s7+W/G9EcR9q9O964a9q0+O8xbY4elV57LX5H6cwze/6AjGCJfGoip5r4KcXfshmLSLYtJxpTNGTJiVmyY/EGAIkEvW13ND6It21xOjU2kON0LLcfS5GIzGhYRjQS/3za8f1l28ljJtYljiWn47hOe6beth3JSnlt+/+faBiX2C9RvuT6lOWD9cd12qc/pHFLpmbhN3CCHKfjjmK6PouJ+oVlxRvsWbbsSExWxJIdibU3Por/dGJW/Jz3UDdILjuOnJid/F0m1suJNzxJfOCSnw3LijcAaS9H4nOS/H3bdvL/SxwzcYzkuvamWombkonvb0kddZJkO7Tu2yTF6wld9neMlHWp+yeOZ0iGYbTPlxRfTu5rxPd32hO1hpFStq5laT9eanm6Pjc98eV4jrdrQzx1+2nZhiLRzsfr+P/ay20YKQ0ijPbzaHS0VbOs+LlvP+fxv514S5l4Q1OnY7nr7yYRE2w7/vu1u3wmkn+DdvdjOI5sR4qUlKt10mxFJ5+kklJDs8YcU7i2RUdrPXJMjzx+jzz5AZkBnzx5fhWWeFQ6xshYA16cmE512t27FXllrY7tbVJLc8fUFX3e9TrBW2JpvYPWtYFnv/v3fZz+dhvw8fo+/OhUWCizqEDelgapvfdgT490G1TdehCvOdGXpJbL6bGQ3df1tFt/r+3z/fd0nSOj++qBnsSe9uvrtd1as/f/2h7PQT+vTf1uH4jBnoPE93lPG/Yf2K/JkyZLkjwlhSp/7yIVnzQufpFkWVJ5eUYvBOv31uvoP7bLCPg15cJT5HfCiu7cq9qjlqLReFsxSbJijuxQJP5oC8uOWbJj8XqjZSl+j8mQgn5bRdPGauw/L5YRDMhpblHd/z6n0P4aeTzx+ziOjHhHAsOQvN6O+0EeT/y+TvvP+D0jU6bPo5JTpsgzc3q8g0IkpuZtB2S3heX1m/IGvfL4443jTJ+no5Fb6iPRWK7ruY3FpJ07paNHFWpzVFcntbY4Hdc1KQ1Tja4t2lJ/SpJpyg7kyQkEZfsC8oaaZTY1dOogIXV0XugU6Jx4x4juvYqcXvfvdKzEajvl/+ph/46ftsyWZhmRcLxhn9cXfxhm99ekLvYXnB2nyy5Opx897N7je+7peY+H6On1vZSz06p+/s+e32aXFspdymX0d4w+ytnrae3v3PS9uvdzdgLH6ImnME9j3rtMlVP80vr1ijSG1FDvqKUlfs8v9Xo+/vduJBurOIbRfo0TP1biekfqcv3Uft3XcU2Xcj3Y5dop9dqv9N2naWf0CLmwNCAxPkCPPvqorrjiCv34xz/W8uXLde+99+q3v/2ttm3b1m3u8a5IjLtry5Ytmj9/fraLAQA5izgLAO4izgKA+4i1AOAu4iwAuIs4mx4nkrMd1XOM/9u//ZuOHj2qW265RdXV1Vq0aJH+/Oc/95sUh/tmzpyZ7SIAQE4jzgKAu4izAOA+Yi0AuIs4CwDuIs5m3qgfDOy6667TO++8o3A4zPDdw8iGDRuyXQQAyGnEWQBwF3EWANxHrAUAdxFnAcBdxNnMG/WJcQAAAAAAAAAAAABAbiMxjmFp8uTJ2S4CAOQ04iwAuIs4CwDuI9YCgLuIswDgLuJs5pEYx7Bkmnw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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 2000x500 with 1 Axes>"
      ],
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1500x600 with 2 Axes>"
      ],
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== Cost Analysis ===\n",
      "RL Operational Cost: 7810.30\n",
      "DPC Operational Cost: 7706.50\n",
      "RL Constraint Violation Cost: 11.57\n",
      "DPC Constraint Violation Cost: 283.80\n"
     ]
    }
   ],
   "execution_count": 100
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "\n",
    "## Conclusion\n",
    "\n",
    "In this notebook, we explored the performance of Safe RL and DPC for controlling a simulated thermal system. Below is a summary of the key findings and insights.\n",
    "\n",
    "\n",
    "> **Note:**  \n",
    "For a fairer comparison between safe RL and DPC, it is recommended to consider a variation of DPC that incorporates a Predictive Safety Filter (PSF). This approach is discussed in:  \n",
    "> *Robust Differentiable Predictive Control with Safety Guarantees: A Predictive Safety Filter Approach* by Shaw Cortez W, Drgona J, Vrabie D, Halappanavar M., 2023 ([arXiv:2311.08496](https://arxiv.org/pdf/2311.08496)).\n",
    "\n",
    "---\n",
    "\n",
    "### Key Steps:\n",
    "\n",
    "1. **Data Preparation**:\n",
    "   - Generated synthetic datasets for training and evaluation using a simulated thermal system.\n",
    "   - Applied normalization to system states, disturbances, and reference bounds for improved stability and convergence.\n",
    "\n",
    "\n",
    "2. **RL and DPC Implementation**:\n",
    "   - Trained an RL agent using a custom reward and cost structure.\n",
    "   - Designed and optimized a neural control policy for DPC.\n",
    "\n",
    "\n",
    "3. **Cost Evaluation**:\n",
    "   - Compared operational and constraint violation costs for RL and DPC across evaluation scenarios.\n",
    "   - Summarized average costs to assess efficiency and constraint adherence on unseen scenarios.\n",
    "\n",
    "---\n",
    "\n",
    "### Key Findings:\n",
    "\n",
    "- **Control Effectiveness**:\n",
    "  - Both RL and DPC effectively maintained temperatures within acceptable bounds in most cases, with comparable performance.\n",
    "  - However, some constraint violations were unavoidable due to randomly generated disturbances and references. For instance, on particularly hot days with a low reference temperature range, even the best control policy couldn’t fully prevent violations. This can affect the training process of RL.\n",
    "\n",
    "- **Operational Costs**:\n",
    "  - RL incurred slightly higher operational costs due to its primal-dual strategy, which balances constraints and rewards.\n",
    "  - DPC was more energy-efficient, prioritizing smoother control actions due to its stronger emphasis on action smoothness in the loss function.\n",
    "\n",
    "- **Constraint Violations**:\n",
    "  - RL had slightly fewer constraint violations than DPC, but the difference was negligible in practice.\n",
    "\n",
    "- **Training Time**:\n",
    "  - RL takes longer to train due to its evaluations and exploration phase. However, it converges quickly, as seen in evaluation plots.\n",
    "  - DPC requires ~350 seconds for 300 epochs, which is comparable to RL’s pure training time.\n",
    "  - Interestingly, RL learned an acceptable policy after seeing only half of the training data, demonstrating strong sample efficiency.\n",
    "\n",
    "---\n",
    "\n",
    "### Pros and Cons: PD-DDPG vs. DPC\n",
    "\n",
    "**PD-DDPG:**\n",
    "- **Pros:** Adaptable to dynamic environments; fewer constraint violations; model-free, making it suitable for complex systems.\n",
    "- **Cons:** Requires longer training; sensitive to reward and cost design and hyperparameters; incurs higher operational costs; Much harder to define the MDP environment for RL agent.\n",
    "\n",
    "**DPC:**\n",
    "- **Pros:** More energy-efficient; smooth control actions; faster training with easier constraint enforcement.\n",
    "- **Cons:** Fixed weights between objective and constraint; Performance highly depends on state-space model accuracy.\n",
    "\n",
    "---\n",
    "\n",
    "### Future Work:\n",
    "\n",
    "- Explore hybrid approaches combining RL and DPC to leverage RL’s adaptability and DPC’s efficiency.\n",
    "- Investigate adaptive tuning strategies for balancing constraint enforcement and energy efficiency.\n"
   ],
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